diff --git a/README.md b/README.md
index 89999f0..b0652df 100644
--- a/README.md
+++ b/README.md
@@ -1,53 +1,46 @@
 # distribution-optimization-py: Optimization-Based GMM Parameter Estimation
 
-A Gaussian Mixture Model (GMM) is a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters. The GMM is here rephrased as an optimization problem and solved using a Hierarchic Memetic Strategy (HMS). This approach diverges from the traditional Likelihood Maximization, Expectation Maximization approach. Instead, it introduces a fitness function that combines the chi-square test, used for analyzing distributions, with an innovative metric designed to estimate the overlapping area under the curves among various Gaussian distributions. This library offers an implementation of [DistributionOptimization](https://cran.r-project.org/web/packages/DistributionOptimization/index.html) in Python (with significant improvements).
+## Installation
 
-### Experiment results
-
-The results of the experiments can be found on the `ela-analysis` branch.
-
-### Quick start
-Install:
+```bash
+git clone git+https://github.com/agh-a2s/distribution-optimization.git
+```
 
+To install dependencies
 ```
-pip install git+https://github.com/WojtAcht/distribution-optimization-py
+poetry install
 ```
 
-Create dataset:
-
-```python
-import numpy as np
-random_state = np.random.RandomState(seed=1)
-textbook_data = np.concatenate(
-    [
-        random_state.normal(-1, 1.5, 350),
-        random_state.normal(0, 1, 500),
-        random_state.normal(3, 0.5, 150),
-    ]
-)
-```
+## Reproducing Experiments and Plots
+
+### Generating Datasets
+
+Synthetic datasets are available in `results` directory. The generation can be reproduced by running `poetry run python3 -m distribution_optimization_py.experiment`.
 
-Fit GMM using HMS and plot results:
+### Benchmarking Optimization Algorithms
 
-```python
-from distribution_optimization_py.gaussian_mixture import GaussianMixture
-gmm = GaussianMixture(n_components=3, algorithm="HMS", random_state=42)
-gmm.fit(textbook_data)
-probabilities = gmm.predict_proba(textbook_data)
-gmm.plot()
+To benchmark optimization algorithms on GMM parameter estimation run `poetry run python3 -m distribution_optimization_py.solver`.
+
+### Reproducing Plots from the Paper
+
+Fig. 1:
+```bash
+poetry run python3 -m distribution_optimization_py.plot_binning_scheme_difference
+```
+Fig. 2:
+```bash
+poetry run python3 -m distribution_optimization_py.experiment.plot
 ```
 
-![GMM Plot](images/plot.png)
+Fig. 3:
+```bash
+poetry run python3 -m distribution_optimization_py.solver.plot
+```
 
-### Supported optimization algorithms
+Fig. 3:
+```bash
+poetry run python3 -m distribution_optimization_py.compare_results
+```
 
-| algorithm | Description                                                  | library   |
-|-----------|--------------------------------------------------------------|-----------|
-| "HMS"     | Hierarchic Memetic Strategy                                  | `pyhms`   |
-| "CMA-ES"  | Covariance Matrix Adaptation Evolution Strategy              | `pycma`   |
-| "GA"      | DistributionOptimization evolutionary algorithm              | `leap_ec` |
-| "DE"    | Differential Evolution iL-SHADE                                | `pyade.ilshade` |
+All these scripts will create plots in the `images` directory.
 
-### Relevant literature
-- Lerch, F., Ultsch, A. & Lötsch, J. Distribution Optimization: An evolutionary algorithm to separate Gaussian mixtures. Sci Rep 10, 648 (2020). doi: [10.1038/s41598-020-57432-w](https://doi.org/10.1038/s41598-020-57432-w)
-- J. Sawicki, M. Łoś, M. Smołka, J. Alvarez-Aramberri. Using Covariance Matrix Adaptation Evolutionary Strategy to boost the search accuracy in hierarchic memetic computations. Journal of computational science, 34, 48-54, 2019. doi: [10.1016/j.jocs.2019.04.005](https://doi.org/10.1016/j.jocs.2019.04.005)
diff --git a/distribution_optimization_py/compare_results.py b/distribution_optimization_py/compare_results.py
new file mode 100644
index 0000000..da2963f
--- /dev/null
+++ b/distribution_optimization_py/compare_results.py
@@ -0,0 +1,155 @@
+import random
+
+import numpy as np
+import pandas as pd
+import plotly.graph_objects as go
+import plotly.io as pio
+from distribution_optimization_py.datasets import DATASETS
+from distribution_optimization_py.gaussian_mixture import GaussianMixture
+from distribution_optimization_py.problem import GaussianMixtureProblem
+from distribution_optimization_py.solver import GASolver, iLSHADESolver
+from plotly.subplots import make_subplots
+
+pio.kaleido.scope.mathjax = None
+
+np.random.seed(42)
+random.seed(42)
+
+DATASET_NAME_TO_TITLE = {
+    "truck_driving_data": "Truck Driving",
+    "mixture3": "Mixture 3",
+    "textbook_data": "Textbook Data",
+    "iris_ica": "Iris ICA",
+    "chromatogram_time": "Chromatogram Time",
+    "atmosphere_data": "Atmosphere Data",
+}
+
+if __name__ == "__main__":
+    datasets = [DATASETS[0], DATASETS[3], DATASETS[4]]
+    fig = make_subplots(
+        rows=3,
+        cols=1,
+        subplot_titles=[DATASET_NAME_TO_TITLE.get(d.name) for d in datasets],
+        vertical_spacing=0.08,
+    )
+
+    for subplot_idx, dataset in enumerate(datasets, 1):
+        data = dataset.data
+        nr_of_modes = dataset.nr_of_modes
+        old_problem = GaussianMixtureProblem(
+            data=data,
+            nr_of_modes=nr_of_modes,
+            bin_type="equal_width",
+            bin_number_method="keating",
+        )
+        new_problem = GaussianMixtureProblem(
+            data=data,
+            nr_of_modes=nr_of_modes,
+            bin_type="equal_probability",
+            bin_number_method="mann_wald",
+        )
+        old_solver = GASolver()
+        new_solver = iLSHADESolver()
+        old_solutions = []
+        new_solutions = []
+
+        for i in range(10):
+            random_state = 42 + i
+            old_solution = old_solver(
+                old_problem, max_n_evals=10000, random_state=random_state
+            )
+            new_solution = new_solver(
+                new_problem, max_n_evals=10000, random_state=random_state
+            )
+            old_solutions.append(old_solution.genome)
+            new_solutions.append(new_solution.genome)
+
+        all_solutions = old_solutions + new_solutions
+        all_labels = [f"Keating-{i+1}" for i in range(10)] + [
+            f"Mann-Wald-{i+1}" for i in range(10)
+        ]
+        bins = 75
+
+        gmms = [
+            GaussianMixture(n_components=nr_of_modes, random_state=1).set_params(
+                data, solution
+            )
+            for solution in all_solutions
+        ]
+
+        x = np.linspace(data.min(), data.max(), 1000)
+        pdfs = [gmm.score_samples(x) for gmm in gmms]
+        hist_data = np.histogram(data, bins=bins, density=True)
+
+        data_color = "rgba(31, 119, 180, 0.3)"
+        keating_color = "#ff7f0e"
+        mann_wald_color = "#2ca02c"
+
+        fig.add_trace(
+            go.Bar(
+                x=hist_data[1][:-1],
+                y=hist_data[0],
+                opacity=0.7,
+                name="Empirical Data",
+                marker_color=data_color,
+                marker_line_width=0.2,
+                legendgroup="data",
+                showlegend=(subplot_idx == 1),
+            ),
+            row=subplot_idx,
+            col=1,
+        )
+
+        for i, (pdf, label) in enumerate(zip(pdfs, all_labels)):
+            dash_pattern = "dashdot"
+            color = keating_color if "Keating" in label else mann_wald_color
+
+            show_legend = subplot_idx == 1 and (
+                (i == 0 and "Keating" in label) or (i == 10 and "Mann-Wald" in label)
+            )
+            legend_group = (
+                "GA + Keating" if "Keating" in label else "iLSHADE + Mann-Wald"
+            )
+            legend_name = (
+                "GA + Keating" if "Keating" in label else "iLSHADE + Mann-Wald"
+            )
+
+            fig.add_trace(
+                go.Scatter(
+                    x=x.flatten(),
+                    y=pdf,
+                    mode="lines",
+                    name=legend_name if show_legend else None,
+                    legendgroup=legend_group,
+                    showlegend=show_legend,
+                    line=dict(color=color, dash=dash_pattern, width=1.5),
+                ),
+                row=subplot_idx,
+                col=1,
+            )
+
+    fig.update_layout(
+        height=1200,
+        width=1400,
+        showlegend=True,
+        template="plotly_white",
+        font=dict(size=20),
+        legend=dict(
+            yanchor="top",
+            y=0.99,
+            xanchor="left",
+            x=1.02,
+            font=dict(size=18),
+        ),
+        margin=dict(l=60, r=100, t=60, b=60),
+    )
+
+    # Increase font size for subplot titles
+    for i in range(len(fig.layout.annotations)):
+        fig.layout.annotations[i].font.size = 22
+
+    for i in range(1, 4):
+        fig.update_xaxes(title_text="X" if i == 3 else None, row=i, col=1)
+        fig.update_yaxes(title_text="Density" if i == 2 else None, row=i, col=1)
+
+    fig.write_image(f"./images/comparison_plot.eps", scale=2)
diff --git a/distribution_optimization_py/experiment/__init__.py b/distribution_optimization_py/experiment/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/distribution_optimization_py/experiment/__main__.py b/distribution_optimization_py/experiment/__main__.py
new file mode 100644
index 0000000..b60cf42
--- /dev/null
+++ b/distribution_optimization_py/experiment/__main__.py
@@ -0,0 +1,26 @@
+from multiprocessing import Pool
+
+from .method import run_experiment_for_difficult_examples, run_experiment_for_nr_of_components
+
+if __name__ == "__main__":
+    components_to_test = list(range(2, 11))
+    components_to_nr_of_datasets = {i: 100 for i in components_to_test}
+    min_overlap_value = 0.0
+    max_overlap_value = 0.49
+    results_dir_name = "results"
+    arg_list = [
+        (
+            nr_of_components,
+            components_to_nr_of_datasets[nr_of_components],
+            min_overlap_value,
+            max_overlap_value,
+            results_dir_name,
+        )
+        for nr_of_components in components_to_test
+    ]
+
+    with Pool(10) as p:
+        p.starmap(
+            run_experiment_for_nr_of_components,
+            arg_list,
+        )
diff --git a/distribution_optimization_py/experiment/method.py b/distribution_optimization_py/experiment/method.py
new file mode 100644
index 0000000..3789489
--- /dev/null
+++ b/distribution_optimization_py/experiment/method.py
@@ -0,0 +1,197 @@
+import os
+from dataclasses import dataclass
+from time import perf_counter
+from typing import Callable
+
+import numpy as np
+import pandas as pd
+from distribution_optimization_py.problem import GaussianMixtureProblem
+from distribution_optimization_py.solver import GASolver, Solver, iLSHADESolver
+from distribution_optimization_py.solver.em import EMSolver
+from distribution_optimization_py.synthetic_datasets import (
+    generate_difficult_mixture_parameters,
+    generate_mixture_parameters,
+)
+from distribution_optimization_py.utils import js_div, kl_div
+
+RESULTS_DIR_NAME = "results"
+MIN_OVERLAP_VALUE = 0.0
+MAX_OVERLAP_VALUE = 0.5
+
+
+@dataclass
+class Method:
+    solver: Solver
+    bin_type: str
+    bin_number_method: str
+    name: str
+    use_correction: bool = False
+
+
+METHODS = [
+    Method(
+        solver=GASolver(),
+        bin_type="equal_width",
+        bin_number_method="keating",
+        use_correction=True,
+        name="DO",
+    ),
+    Method(
+        solver=GASolver(),
+        bin_type="equal_probability",
+        bin_number_method="mann_wald",
+        use_correction=True,
+        name="GA Mann-Wald",
+    ),
+    Method(
+        solver=iLSHADESolver(),
+        bin_type="equal_probability",
+        bin_number_method="mann_wald",
+        use_correction=True,
+        name="iLSHADE Mann-Wald",
+    ),
+    Method(
+        solver=EMSolver(),
+        bin_type="equal_probability",
+        bin_number_method="mann_wald",
+        use_correction=True,
+        name="EM",
+    ),
+]
+
+
+def run_experiment_for_nr_of_components(
+    nr_of_components: int,
+    nr_of_datasets: int,
+    min_overlap_value: float = MIN_OVERLAP_VALUE,
+    max_overlap_value: float = MAX_OVERLAP_VALUE,
+    results_dir_name: str = RESULTS_DIR_NAME,
+    dataset_start_idx: int = 0,
+    nr_of_seeds: int = 10,
+    methods: list[Method] = METHODS,
+    max_n_evals: int = 10000,
+    mean_range: tuple[float] = [0, 1],
+    name_to_metric: dict[str, Callable] = {
+        "KL": kl_div,
+        "JS": js_div,
+    },
+) -> None:
+    os.makedirs(f"{results_dir_name}/{nr_of_components}", exist_ok=True)
+    rows = []
+    existing_results = (
+        pd.read_csv(f"{results_dir_name}/{nr_of_components}/results.csv", index_col=0)
+        if os.path.exists(f"{results_dir_name}/{nr_of_components}/results.csv")
+        else None
+    )
+    for mixture_idx, (parameters, dataset) in enumerate(
+        generate_mixture_parameters(
+            n_mixtures=nr_of_datasets,
+            n_components=nr_of_components,
+            mean_range=mean_range,
+            min_overlap_value=min_overlap_value,
+            max_overlap_value=max_overlap_value,
+        )
+    ):
+        dataset_idx = dataset_start_idx + mixture_idx
+        np.save(f"{results_dir_name}/{nr_of_components}/dataset_{dataset_idx}.npy", dataset)
+        np.save(
+            f"{results_dir_name}/{nr_of_components}/parameters_{dataset_idx}.npy",
+            parameters,
+        )
+        dataset_start = perf_counter()
+        for random_state in range(1, nr_of_seeds + 1):
+            for method in methods:
+                start = perf_counter()
+                problem = GaussianMixtureProblem(
+                    dataset,
+                    nr_of_components,
+                    bin_type=method.bin_type,
+                    bin_number_method=method.bin_number_method,
+                )
+                solution = method.solver(problem, max_n_evals=max_n_evals, random_state=random_state)
+                time = perf_counter() - start
+                row = {
+                    "method": method.name,
+                    "solution": solution.genome,
+                    "time": time,
+                    "fitness": solution.fitness,
+                    "optimal_solution": parameters,
+                    "optimal_fitness": problem(parameters),
+                    "nr_of_components": nr_of_components,
+                    "dataset_idx": dataset_idx,
+                    "random_state": random_state,
+                } | {name: metric(parameters, solution.genome) for name, metric in name_to_metric.items()}
+                if time > 10:
+                    print(
+                        f"Nr of components {nr_of_components} dataset {dataset_idx} seed {random_state} took {time} seconds"
+                    )
+                rows.append(row)
+        print(f"Dataset {dataset_idx} took {perf_counter() - dataset_start} seconds")
+        all_results = (
+            pd.concat([existing_results, pd.DataFrame(rows)]) if existing_results is not None else pd.DataFrame(rows)
+        )
+        all_results.to_csv(f"{results_dir_name}/{nr_of_components}/results.csv")
+
+
+def run_experiment_for_difficult_examples(
+    nr_of_components: int,
+    nr_of_datasets: int,
+    results_dir_name: str = RESULTS_DIR_NAME,
+    dataset_start_idx: int = 0,
+    nr_of_seeds: int = 10,
+    methods: list[Method] = METHODS,
+    max_n_evals: int = 10000,
+    name_to_metric: dict[str, Callable] = {
+        "KL": kl_div,
+        "JS": js_div,
+    },
+) -> None:
+    os.makedirs(f"{results_dir_name}/{nr_of_components}", exist_ok=True)
+    rows = []
+    existing_results = (
+        pd.read_csv(f"{results_dir_name}/{nr_of_components}/results.csv", index_col=0)
+        if os.path.exists(f"{results_dir_name}/{nr_of_components}/results.csv")
+        else None
+    )
+    for mixture_idx, (parameters, dataset) in enumerate(
+        generate_difficult_mixture_parameters(n_mixtures=nr_of_datasets)
+    ):
+        dataset_idx = dataset_start_idx + mixture_idx
+        np.save(
+            f"{results_dir_name}/{nr_of_components}/dataset_{dataset_idx}.npy",
+            dataset,
+        )
+        np.save(
+            f"{results_dir_name}/{nr_of_components}/parameters_{dataset_idx}.npy",
+            parameters,
+        )
+        dataset_start = perf_counter()
+        for random_state in range(1, nr_of_seeds + 1):
+            for method in methods:
+                start = perf_counter()
+                problem = GaussianMixtureProblem(
+                    dataset,
+                    nr_of_components,
+                    bin_type=method.bin_type,
+                    bin_number_method=method.bin_number_method,
+                )
+                solution = method.solver(problem, max_n_evals=max_n_evals, random_state=random_state)
+                time = perf_counter() - start
+                row = {
+                    "method": method.name,
+                    "solution": solution.genome,
+                    "time": time,
+                    "fitness": solution.fitness,
+                    "optimal_solution": parameters,
+                    "optimal_fitness": problem(parameters),
+                    "dataset_idx": dataset_idx,
+                    "random_state": random_state,
+                } | {name: metric(parameters, solution.genome) for name, metric in name_to_metric.items()}
+                if time > 10:
+                    print(f"Dataset {dataset_idx} seed {random_state} took {time} seconds")
+                rows.append(row)
+        print(f"Dataset {dataset_idx} took {perf_counter() - dataset_start} seconds")
+        all_results = (
+            pd.concat([existing_results, pd.DataFrame(rows)]) if existing_results is not None else pd.DataFrame(rows)
+        )
+        all_results.to_csv(f"{results_dir_name}/{nr_of_components}/results.csv")
diff --git a/distribution_optimization_py/experiment/plot.py b/distribution_optimization_py/experiment/plot.py
new file mode 100644
index 0000000..a829b93
--- /dev/null
+++ b/distribution_optimization_py/experiment/plot.py
@@ -0,0 +1,168 @@
+import pandas as pd
+import plotly.io as pio
+import plotly.subplots as sp
+
+pio.kaleido.scope.mathjax = None
+
+if __name__ == "__main__":
+    results_dir_name = "results"
+
+    rows_rank = []
+    rows_value = []
+    all_js_values = {"DO": [], "GA Mann-Wald": [], "iLSHADE Mann-Wald": [], "EM": []}
+    for nr_of_components in range(2, 11):
+        results = pd.read_csv(
+            f"./{results_dir_name}/{nr_of_components}/results.csv", index_col=0
+        )
+        dict_result_rank = (
+            results[["method", "nr_of_components", "dataset_idx", "JS"]]
+            .groupby(["method", "nr_of_components", "dataset_idx"])
+            .mean()
+            .groupby(["dataset_idx"])
+            .rank(method="min")
+            .groupby("method")
+            .mean()
+            .sort_values("JS")
+            .to_dict()["JS"]
+        )
+        rows_rank.append(dict_result_rank | {"nr_of_components": nr_of_components})
+
+        dict_result_value = (
+            results[["method", "nr_of_components", "dataset_idx", "JS"]]
+            .groupby(["method", "nr_of_components", "dataset_idx"])
+            .mean()
+            .groupby("method")
+            .mean()
+            .sort_values("JS")
+            .to_dict()["JS"]
+        )
+        rows_value.append(dict_result_value | {"nr_of_components": nr_of_components})
+
+        for method in ["DO", "GA Mann-Wald", "iLSHADE Mann-Wald", "EM"]:
+            method_js_values = (
+                results[results["method"] == method]
+                .groupby("dataset_idx")["JS"]
+                .mean()
+                .tolist()
+            )
+            all_js_values[method].append(
+                {"values": method_js_values, "nr_of_components": nr_of_components}
+            )
+
+    fig = sp.make_subplots(
+        rows=2,
+        cols=2,
+        subplot_titles=("", "", ""),
+        row_heights=[
+            0.35,
+            0.65,
+        ],  # Adjusted row heights to reduce space between subplots
+        specs=[[{}, {}], [{"colspan": 2}, None]],
+        vertical_spacing=0.1,  # Added vertical spacing parameter to reduce gap
+    )
+
+    colors = {
+        "DO": "#1f77b4",
+        "GA Mann-Wald": "#ff7f0e",
+        "iLSHADE Mann-Wald": "#2ca02c",
+        "EM": "#d62728",
+    }
+
+    df_rank = pd.DataFrame(rows_rank).set_index("nr_of_components")[
+        ["DO", "GA Mann-Wald", "iLSHADE Mann-Wald", "EM"]
+    ]
+    df_value = pd.DataFrame(rows_value).set_index("nr_of_components")[
+        ["DO", "GA Mann-Wald", "iLSHADE Mann-Wald", "EM"]
+    ]
+    column_to_name = {
+        "DO": "GA + Keating",
+        "GA Mann-Wald": "GA + Mann-Wald",
+        "iLSHADE Mann-Wald": "iLSHADE + Mann-Wald",
+        "EM": "EM",
+    }
+
+    for method in df_rank.columns:
+        fig.add_trace(
+            dict(
+                type="scatter",
+                x=df_rank.index,
+                y=df_rank[method],
+                name=column_to_name[method],
+                line=dict(color=colors[method]),
+                showlegend=True,
+            ),
+            row=1,
+            col=1,
+        )
+        fig.add_trace(
+            dict(
+                type="scatter",
+                x=df_value.index,
+                y=df_value[method],
+                name=column_to_name[method],
+                line=dict(color=colors[method]),
+                showlegend=False,
+            ),
+            row=1,
+            col=2,
+        )
+
+        x_coords = []
+        y_coords = []
+        for data in all_js_values[method]:
+            method_offset = list(df_rank.columns).index(method) * 0.2 - 0.3
+            x_coords.extend(
+                [data["nr_of_components"] + method_offset] * len(data["values"])
+            )
+            y_coords.extend(data["values"])
+
+        fig.add_trace(
+            dict(
+                type="box",
+                x=x_coords,
+                y=y_coords,
+                name=column_to_name[method],
+                marker=dict(color=colors[method]),
+                boxpoints="all",  # Show all points
+                jitter=0.3,  # Add random jitter to points for better visibility
+                pointpos=0,  # Center the points
+                line=dict(color=colors[method]),
+                fillcolor="white",
+                opacity=0.6,
+                showlegend=False,
+            ),
+            row=2,
+            col=1,
+        )
+
+    fig.update_layout(
+        height=800,
+        width=1200,
+        showlegend=True,
+        template="plotly_white",
+        font=dict(size=18),
+        legend=dict(
+            yanchor="top",
+            y=0.99,
+            xanchor="left",
+            x=1.0,
+        ),
+        margin=dict(l=60, r=120, t=40, b=60),
+    )
+
+    fig.update_xaxes(title_text="Number of Components", row=1, col=1)
+    fig.update_xaxes(title_text="Number of Components", row=1, col=2)
+    fig.update_xaxes(title_text="Number of Components", row=2, col=1)
+    fig.update_yaxes(title_text="Average Rank", row=1, col=1)
+    fig.update_yaxes(title_text="Average JSD Value", row=1, col=2)
+    fig.update_yaxes(title_text="JSD Value", row=2, col=1)
+
+    fig.update_xaxes(
+        tickmode="array",
+        ticktext=list(range(2, 11)),
+        tickvals=list(range(2, 11)),
+        row=2,
+        col=1,
+    )
+
+    fig.write_image("./images/js_analysis.eps", scale=2)
diff --git a/distribution_optimization_py/gaussian_mixture.py b/distribution_optimization_py/gaussian_mixture.py
index d45efe2..8e80a4a 100644
--- a/distribution_optimization_py/gaussian_mixture.py
+++ b/distribution_optimization_py/gaussian_mixture.py
@@ -8,7 +8,7 @@
 from .datasets import Dataset
 from .problem import GaussianMixtureProblem
 from .solver import DESolver, GASolver, HMSSolver, Solver
-from .utils import mixture_probability
+from .utils import mixture_probabilities
 
 SOLVER_NAME_TO_CLASS: dict[str, Type[Solver]] = {
     "GA": GASolver,
@@ -46,14 +46,14 @@ def fit(self, X: np.ndarray) -> "GaussianMixture":
         return self
 
     def predict_proba(self, X: np.ndarray) -> np.ndarray:
-        probabilities = [mixture_probability(x, self._means, self._sds, self._weights) for x in X]
+        probabilities = [mixture_probabilities(x, self._means, self._sds, self._weights) for x in X]
         return np.array(probabilities)
 
     def predict(self, X: np.ndarray) -> np.ndarray:
         return np.argmax(self.predict_proba(X), axis=1)
 
     def score_samples(self, X: np.ndarray) -> np.ndarray:
-        likelihood = np.array([mixture_probability(x, self._means, self._sds, self._weights, False) for x in X])
+        likelihood = np.array([mixture_probabilities(x, self._means, self._sds, self._weights, False) for x in X])
         return np.sum(likelihood, axis=1)
 
     def plot(self, num: int | None = 1000, bins: int | None = 30) -> None:
@@ -135,13 +135,13 @@ def compare_solutions_plotly(
     labels: list[str],
     num: int | None = 1000,
     bins: int | None = 30,
+    title: str | None = "Comparison of GMM Fits to 1D Data",
 ) -> None:
     assert len(solutions) == len(labels)
     gmms = [GaussianMixture(n_components=nr_of_modes, random_state=1).set_params(X, solution) for solution in solutions]
 
     x = np.linspace(X.min(), X.max(), num)
     pdfs = [gmm.score_samples(x) for gmm in gmms]
-
     hist_data = np.histogram(X, bins=bins, density=True)
 
     fig = go.Figure()
@@ -167,10 +167,13 @@ def compare_solutions_plotly(
         )
 
     fig.update_layout(
-        title="Histogram and GMM PDF",
+        title=title,
         xaxis_title="X",
         yaxis_title="Density",
-        legend=dict(x=1.2, y=1),
+        legend=dict(y=0.95),
+        barmode="overlay",
+        width=1200,
+        height=800,
     )
 
     fig.show()
diff --git a/distribution_optimization_py/plot_binning_scheme_difference.py b/distribution_optimization_py/plot_binning_scheme_difference.py
new file mode 100644
index 0000000..e3aa9f4
--- /dev/null
+++ b/distribution_optimization_py/plot_binning_scheme_difference.py
@@ -0,0 +1,138 @@
+import numpy as np
+from distribution_optimization_py.problem import GaussianMixtureProblem
+from distribution_optimization_py.solver import iLSHADESolver
+from distribution_optimization_py.synthetic_datasets import (
+    generate_gaussian_mixture_data,
+)
+import random
+from distribution_optimization_py.gaussian_mixture import GaussianMixture
+import plotly.graph_objects as go
+import plotly.io as pio
+
+pio.kaleido.scope.mathjax = None
+
+np.random.seed(42)
+random.seed(42)
+
+# nr_of_modes = 4
+# min = 0.0
+# max = 1.0
+# sigma = (max - min) * 0.01 * 1.5
+# expected_bin_width = (max - min) / 10
+# step_size = expected_bin_width / 4
+# best_solution = np.array([0.5, 0.5, sigma, sigma, min, max])
+# data = generate_gaussian_mixture_data(
+#     means=np.array([min, (min + max) / 3, 2 * (min + max) / 3, max]),
+#     stds=np.array([sigma, sigma, sigma, sigma]),
+#     weights=np.array([1 / 4, 1 / 4, 1 / 4, 1 / 4]),
+#     n_samples=1000,
+# )
+
+nr_of_modes = 3
+min = 0.0
+max = 1.0
+sigma = (max - min) * 0.01 * 1.5
+expected_bin_width = (max - min) / 10
+step_size = expected_bin_width / 4
+data = generate_gaussian_mixture_data(
+    means=np.array([min, (min + max) / 2, max]),
+    stds=np.array([sigma, sigma, sigma]),
+    weights=np.array([1 / nr_of_modes] * nr_of_modes),
+    n_samples=1000,
+)
+
+old_problem = GaussianMixtureProblem(
+    data=data,
+    nr_of_modes=nr_of_modes,
+    bin_type="equal_width",
+    bin_number_method="keating",
+)
+new_problem = GaussianMixtureProblem(
+    data=data,
+    nr_of_modes=nr_of_modes,
+    bin_type="equal_probability",
+    bin_number_method="mann_wald",
+)
+
+solver = iLSHADESolver()
+old_solutions = []
+new_solutions = []
+
+for i in range(10):
+    random_state = i + 42
+    np.random.seed(random_state)
+    random.seed(random_state)
+    old_solution = solver(old_problem, max_n_evals=10000, random_state=random_state)
+    new_solution = solver(new_problem, max_n_evals=10000, random_state=random_state)
+    old_solutions.append(old_solution.genome)
+    new_solutions.append(new_solution.genome)
+
+all_solutions = old_solutions + new_solutions
+all_labels = [f"Keating-{i+1}" for i in range(10)] + [
+    f"Mann-Wald-{i+1}" for i in range(10)
+]
+bins = 100
+
+gmms = [
+    GaussianMixture(n_components=nr_of_modes, random_state=1).set_params(data, solution)
+    for solution in all_solutions
+]
+
+x = np.linspace(data.min(), data.max(), 1000)
+pdfs = [gmm.score_samples(x) for gmm in gmms]
+hist_data = np.histogram(data, bins=bins, density=True)
+
+data_color = "rgba(31, 119, 180, 1.0)"  # Blue color with full opacity
+keating_color = "#ff7f0e"  # Orange
+mann_wald_color = "#2ca02c"  # Green
+
+fig = go.Figure()
+
+fig.add_trace(
+    go.Bar(
+        x=hist_data[1][:-1],
+        y=hist_data[0],
+        opacity=0.8,
+        name="Empirical Data",
+        marker_color=data_color,
+        marker_line_width=0.5,
+        legendgroup="data",
+    )
+)
+
+for i, (pdf, label) in enumerate(zip(pdfs, all_labels)):
+    dash_pattern = "dashdot"
+    color = keating_color if "Keating" in label else mann_wald_color
+
+    show_legend = (i == 0 and "Keating" in label) or (i == 10 and "Mann-Wald" in label)
+    legend_group = "Keating" if "Keating" in label else "Mann-Wald"
+    legend_name = "Keating" if "Keating" in label else "Mann-Wald"
+
+    fig.add_trace(
+        go.Scatter(
+            x=x.flatten(),
+            y=pdf,
+            mode="lines",
+            name=legend_name if show_legend else None,
+            legendgroup=legend_group,
+            showlegend=show_legend,
+            line=dict(color=color, dash=dash_pattern, width=2),
+        )
+    )
+
+fig.update_layout(
+    height=600,
+    width=1200,
+    showlegend=True,
+    template="plotly_white",
+    font=dict(size=18),
+    legend=dict(yanchor="top", y=0.99, xanchor="left", x=1.0),
+    margin=dict(l=60, r=120, t=80, b=60),
+    barmode="overlay",
+)
+
+fig.update_yaxes(title_text="Density")
+
+# fig.show()
+fig.write_image("./images/bin_comparison.eps", scale=2)
+fig.write_image("./images/bin_comparison.png", scale=2)
diff --git a/distribution_optimization_py/problem.py b/distribution_optimization_py/problem.py
index e5aa7b8..b1ee14d 100644
--- a/distribution_optimization_py/problem.py
+++ b/distribution_optimization_py/problem.py
@@ -18,7 +18,7 @@
     simplex_to_reals,
     unscale_uniformly_simplex,
 )
-from .utils import bin_prob_for_mixtures, optimal_no_bins
+from .utils import bin_prob_for_mixtures, mann_wald_number_of_bins, max_chi2_number_of_bins, optimal_no_bins
 
 INFINITY = 1000000  # TODO: use np.inf instead of 1000000
 DEFAULT_NR_OF_KERNELS = 40
@@ -26,6 +26,12 @@
 DEFAULT_LOWER = -5
 DEFAULT_UPPER = 5
 
+NAME_TO_BIN_NUMBER_METHOD = {
+    "mann_wald": mann_wald_number_of_bins,
+    "keating": optimal_no_bins,
+    "max_chi2": max_chi2_number_of_bins,
+}
+
 
 class GaussianMixtureProblem:
     def __init__(
@@ -35,24 +41,18 @@ def __init__(
         nr_of_kernels: int | None = DEFAULT_NR_OF_KERNELS,
         overlap_tolerance: float | None = DEFAULT_OVERLAP_TOLERANCE,
         id: str | None = None,
-        nr_of_bins: int | None = None,
-        # TODO:
-        # Investigate Jenks’ Natural Breaks
-        # and Bayesian Blocks hyperparameters
-        # add quantiles with interpolation
-        bin_selection_method: Literal["bayesian_blocks", "quantiles", "equal_width"] = "equal_width",
+        bin_type: Literal["equal_probability", "equal_width"] = "equal_width",
+        bin_number_method: Literal["mann_wald", "keating", "max_chi2"] = "keating",
+        use_correction: bool = True,
     ):
         self.data = data
         self.nr_of_modes = nr_of_modes
         self.N = len(data)
-        if bin_selection_method == "bayesian_blocks":
-            self.breaks = bayesian_blocks(data, fitness="events", p0=0.5)
+        self.nr_of_bins = NAME_TO_BIN_NUMBER_METHOD[bin_number_method](data)
+        if bin_type == "equal_probability":
+            self.breaks = pd.qcut(data, self.nr_of_bins + 1, retbins=True, duplicates="drop")[1]
             self.nr_of_bins = len(self.breaks) - 1
-        elif bin_selection_method == "quantiles":
-            self.nr_of_bins = optimal_no_bins(data) if nr_of_bins is None else nr_of_bins
-            self.breaks = pd.qcut(data, self.nr_of_bins, retbins=True)[1]
         else:
-            self.nr_of_bins = optimal_no_bins(data) if nr_of_bins is None else nr_of_bins
             self.breaks = np.linspace(np.min(data), np.max(data), self.nr_of_bins + 1)
         self.observed_bins, _ = np.histogram(data, self.breaks)
         self.nr_of_kernels = nr_of_kernels
@@ -64,6 +64,7 @@ def __init__(
         self.lower = self.data_lower
         self.upper = self.data_upper
         self.id = id
+        self.use_correction = use_correction
 
     def __call__(self, x: np.ndarray) -> float:
         weights = x[: self.nr_of_modes]
@@ -89,9 +90,9 @@ def overlap_error_by_density(self, means: np.ndarray, sds: np.ndarray, weights:
 
     def similarity_error(self, means: np.ndarray, sds: np.ndarray, weights: np.ndarray) -> float:
         estimated_bins = bin_prob_for_mixtures(means, sds, weights, self.breaks) * self.N
-        norm = np.maximum(estimated_bins, 1)
+        norm = np.maximum(estimated_bins, 1) if self.use_correction else estimated_bins
         diffs_sq = (self.observed_bins - estimated_bins) ** 2
-        diffs_sq = np.where(diffs_sq < 4, 0, diffs_sq)
+        diffs_sq = np.where(diffs_sq < 4, 0, diffs_sq) if self.use_correction else diffs_sq
         return np.sum(diffs_sq / norm) / self.N
 
     def get_bounds(self) -> tuple[np.ndarray, np.ndarray]:
@@ -175,34 +176,6 @@ def fix(self, x: np.ndarray) -> np.ndarray:
         x[2 * self.nr_of_modes :] = x[2 * self.nr_of_modes :][means_order]
         return x
 
-    @classmethod
-    def create_problem(
-        cls,
-        x: np.ndarray,
-        nr_of_kernels: int | None = DEFAULT_NR_OF_KERNELS,
-        overlap_tolerance: float | None = DEFAULT_OVERLAP_TOLERANCE,
-        id: str | None = None,
-        nr_of_bins: int | None = None,
-    ) -> "GaussianMixtureProblem":
-        random_state = np.random.RandomState(seed=1)
-        nr_of_modes = int(len(x) / 3)
-        means = x[2 * nr_of_modes :]
-        sds = x[nr_of_modes : 2 * nr_of_modes]
-        weights = x[:nr_of_modes]
-        total_data_size = 100 * nr_of_modes
-        nr_of_samples = (weights * total_data_size).astype(int)
-        random_data = np.concatenate(
-            [random_state.normal(mean, sd, n) for mean, sd, n in zip(means, sds, nr_of_samples)]
-        )
-        return cls(
-            random_data,
-            nr_of_modes,
-            nr_of_kernels=nr_of_kernels,
-            overlap_tolerance=overlap_tolerance,
-            nr_of_bins=nr_of_bins,
-            id=id,
-        )
-
 
 class LinearlyScaledGaussianMixtureProblem(GaussianMixtureProblem):
     def __init__(
diff --git a/distribution_optimization_py/solver/__init__.py b/distribution_optimization_py/solver/__init__.py
index db3fee9..e118764 100644
--- a/distribution_optimization_py/solver/__init__.py
+++ b/distribution_optimization_py/solver/__init__.py
@@ -1,4 +1,4 @@
-from .de import DESolver, iLSHADESolver
+from .de import DESolver, JADESolver, LSHADESolver, SHADESolver, iLSHADESolver
 from .ga import GASolver
 from .hms import HMSSolver
 from .protocol import Solver
diff --git a/distribution_optimization_py/solver/__main__.py b/distribution_optimization_py/solver/__main__.py
new file mode 100644
index 0000000..38bb27e
--- /dev/null
+++ b/distribution_optimization_py/solver/__main__.py
@@ -0,0 +1,56 @@
+from distribution_optimization_py.evaluator.evaluator import ProblemEvaluator
+import os
+from distribution_optimization_py.solver import (
+    iLSHADESolver,
+    SHADESolver,
+    GASolver,
+    LSHADESolver,
+    DESolver,
+)
+from distribution_optimization_py.problem import (
+    GaussianMixtureProblem,
+)
+import numpy as np
+
+if __name__ == "__main__":
+    solvers = [
+        iLSHADESolver(),
+        DESolver(),
+        SHADESolver(),
+        GASolver(),
+        LSHADESolver(),
+    ]
+
+    synthetic_problems = []
+    synthetic_problems_solutions = []
+    synthetic_problem_id_to_fitness = {}
+    results_dir_name = "./results"
+    for nr_of_components in os.listdir(results_dir_name):
+        for dataset_name in os.listdir(f"{results_dir_name}/{nr_of_components}"):
+            if not dataset_name.startswith("dataset_"):
+                continue
+            dataset_idx = dataset_name.split("_")[1].replace(".npy", "")
+            dataset = np.load(
+                f"{results_dir_name}/{nr_of_components}/dataset_{dataset_idx}.npy"
+            )
+            parameters = np.load(
+                f"{results_dir_name}/{nr_of_components}/parameters_{dataset_idx}.npy"
+            )
+            id = f"synthetic_{nr_of_components}_{dataset_idx}"
+            problem = GaussianMixtureProblem(
+                data=dataset,
+                nr_of_modes=int(nr_of_components),
+                id=id,
+                bin_type="equal_probability",
+                bin_number_method="mann_wald",
+            )
+            synthetic_problems_solutions.append(parameters)
+            synthetic_problems.append(problem)
+            synthetic_problem_id_to_fitness[id] = problem(parameters)
+
+    evaluator = ProblemEvaluator(
+        solvers=solvers, problems=synthetic_problems, max_n_evals=10000, seed_count=10
+    )
+    results = evaluator()
+
+    results[0].to_csv(f"{results_dir_name}/optimization_results.csv")
diff --git a/distribution_optimization_py/solver/plot.py b/distribution_optimization_py/solver/plot.py
new file mode 100644
index 0000000..e2ecc3f
--- /dev/null
+++ b/distribution_optimization_py/solver/plot.py
@@ -0,0 +1,151 @@
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import pandas as pd
+import plotly.io as pio
+import numpy as np
+
+pio.kaleido.scope.mathjax = None
+
+if __name__ == "__main__":
+    pd.options.plotting.backend = "plotly"
+
+    results_dir_name = "./results"
+    results_df = pd.read_csv(f"{results_dir_name}/optimization_results.csv")
+    df = results_df.pivot(
+        index="problem_id",
+        columns="solver",
+        values=["fitness_mean", "fitness_std", "success_rate"],
+    )
+    df["number_of_components"] = df.index.str.split("_").str[1].astype(int)
+    df["dataset_index"] = df.index.str.split("_").str[2]
+    raw_fitness = df["fitness_mean"].rename(
+        columns={
+            "SHADESolver": "SHADE",
+            "GASolver": "GA",
+            "LSHADESolver": "LSHADE",
+            "DESolver": "DE",
+            "iLSHADESolver": "iLSHADE",
+            "JADESolver": "JADE",
+        }
+    )
+
+    problem_ranks = raw_fitness.rank(axis=1)
+
+    grouped_ranks = problem_ranks.groupby(df["number_of_components"]).mean()
+    grouped_fitness = raw_fitness.groupby(df["number_of_components"]).mean()
+
+    colors = {
+        "DE": "#1f77b4",
+        "GA": "#ff7f0e",
+        "SHADE": "#2ca02c",
+        "LSHADE": "#d62728",
+        "iLSHADE": "#9467bd",
+        "JADE": "#8c564b",
+    }
+
+    fig = go.Figure()
+    fig = make_subplots(rows=1, cols=2)
+
+    for method in grouped_ranks.columns:
+        fig.add_trace(
+            go.Scatter(
+                x=grouped_ranks.index,
+                y=grouped_ranks[method],
+                name=method,
+                line=dict(color=colors[method]),
+                showlegend=True,
+            ),
+            row=1,
+            col=1,
+        )
+        fig.add_trace(
+            go.Scatter(
+                x=grouped_fitness.index,
+                y=grouped_fitness[method],
+                name=method,
+                line=dict(color=colors[method]),
+                showlegend=False,
+            ),
+            row=1,
+            col=2,
+        )
+
+    fig.update_layout(
+        height=400,
+        width=1200,
+        showlegend=True,
+        template="plotly_white",
+        font=dict(size=18),
+        legend=dict(yanchor="top", y=0.99, xanchor="left", x=1.0),
+        margin=dict(l=60, r=120, t=40, b=60),
+    )
+
+    fig.update_xaxes(title_text="Number of Components", row=1, col=1)
+    fig.update_xaxes(title_text="Number of Components", row=1, col=2)
+    fig.update_yaxes(title_text="Average Rank", row=1, col=1)
+    fig.update_yaxes(title_text="Average Function Value", row=1, col=2)
+
+    fig.write_image("./images/optimization_methods_analysis.eps")
+
+    # methods = raw_fitness.columns.tolist()
+    # n_methods = len(methods)
+    # win_matrix = np.zeros((n_methods, n_methods))
+    # total_problems = len(raw_fitness)
+
+    # for idx, row in raw_fitness.iterrows():
+    #     for i, method1 in enumerate(methods):
+    #         for j, method2 in enumerate(methods):
+    #             if i == j:
+    #                 win_matrix[i, j] = total_problems
+    #             if i != j and row[method1] <= row[method2]:
+    #                 win_matrix[i, j] += 1
+
+    # win_percentage = (win_matrix / total_problems) * 100
+
+    # fig_heatmap = go.Figure(
+    #     data=go.Heatmap(
+    #         z=win_percentage,
+    #         x=methods,
+    #         y=methods,
+    #         colorscale="Blues",
+    #         text=np.round(win_percentage, 1).astype(str),
+    #         texttemplate="%{text}%",  # Add percentage symbol
+    #         textfont={"size": 14, "color": "black"},
+    #         hoverongaps=False,
+    #         colorbar=dict(
+    #             title="Win Percentage (%)",
+    #             titlefont=dict(size=14, family="Arial"),
+    #             tickfont=dict(size=12, family="Arial"),
+    #             tickformat=".1f",
+    #         ),
+    #     )
+    # )
+
+    # # Configure layout for publication standards
+    # fig_heatmap.update_layout(
+    #     height=600,
+    #     width=700,
+    #     template="plotly_white",
+    #     font=dict(size=16),
+    #     xaxis=dict(
+    #         title=dict(
+    #             text="Algorithm (Column outperforms Row)",
+    #             font=dict(size=16, family="Arial"),
+    #         ),
+    #         tickfont=dict(size=14, family="Arial"),
+    #         tickangle=-30,  # Improve readability of labels
+    #     ),
+    #     yaxis=dict(
+    #         title=dict(text="Algorithm", font=dict(size=16, family="Arial")),
+    #         tickfont=dict(size=14, family="Arial"),
+    #     ),
+    #     margin=dict(l=80, r=80, t=100, b=100),
+    #     paper_bgcolor="white",
+    #     plot_bgcolor="white",
+    # )
+    # fig_heatmap.write_image(
+    #     "./images/algorithm_win_matrix.eps",
+    #     scale=3,
+    #     width=700,
+    #     height=600,
+    # )
diff --git a/distribution_optimization_py/synthetic_datasets.py b/distribution_optimization_py/synthetic_datasets.py
new file mode 100644
index 0000000..5da1f51
--- /dev/null
+++ b/distribution_optimization_py/synthetic_datasets.py
@@ -0,0 +1,143 @@
+import random
+
+import numpy as np
+import pandas as pd
+import plotly.express as px
+from distribution_optimization_py.problem import GaussianMixtureProblem
+
+N_SAMPLES_PER_COMPONENT = 1000
+MIN_WEIGHT = 0.03
+MIN_STD_FRAC = 0.001
+MAX_STD_FRAC = 0.1
+
+
+def sample_weights(n_components: int):
+    weights = np.full(n_components, MIN_WEIGHT)
+    left_weight = 1 - np.sum(weights)
+    weights_surplus = np.random.dirichlet(np.ones(n_components))
+    weights += left_weight * weights_surplus
+    return weights / np.sum(weights)
+
+
+def generate_gaussian_mixture_data(
+    means: np.ndarray,
+    stds: np.ndarray,
+    weights: np.ndarray,
+    n_samples: int | None = 10000,
+):
+    assert len(means) == len(stds) == len(weights), "All parameter lists must have the same length."
+
+    weights = np.array(weights)
+    weights /= weights.sum()
+
+    n_components = len(means)
+
+    component_samples = (weights * n_samples).astype(int)
+
+    data = []
+    for i in range(n_components):
+        component_data = np.random.normal(means[i], stds[i], component_samples[i])
+        data.extend(component_data)
+
+    return np.array(data)
+
+
+def generate_mixture_parameters(
+    n_mixtures: int,
+    n_components: int,
+    mean_range: tuple[float, float],
+    min_overlap_value: float = 0.0,
+    max_overlap_value: float = 0.5,
+    log: bool = False,
+):
+    """
+    This function is responsible for generating mixture parameters for GMMs.
+    The idea is to generate a set of parameters that are feasible for the problem.
+    By feasible, we mean that the parameters are in the domain of the DistributionOptimization problem.
+    This function is implementing a rejection sampling algorithm, which tries to improve
+    efficiency of sampling by generating parameters that are more likely to be feasible.
+    The idea is to generate first and last standard deviations and use them to estimate the range of the data.
+    This way the likelihood of generating parameters that are not feasible is reduced.
+    At the same time it should not introduce a bias in the sampling.
+    """
+    mixture_parameters = []
+    generated_datasets = []
+
+    for idx in range(n_mixtures):
+        random.seed(idx)
+        np.random.seed(idx)
+        while True:
+            weights = sample_weights(n_components)
+            means = np.random.uniform(mean_range[0], mean_range[1], n_components)
+            estimated_data_range = (mean_range[1] - mean_range[0]) + 2 * (mean_range[1] - mean_range[0]) * MAX_STD_FRAC
+            first_std = np.random.uniform(
+                MIN_STD_FRAC * estimated_data_range,
+                MAX_STD_FRAC * estimated_data_range,
+            )
+            last_std = np.random.uniform(
+                MIN_STD_FRAC * estimated_data_range,
+                MAX_STD_FRAC * estimated_data_range,
+            )
+            estimated_data_range = (mean_range[1] - mean_range[0]) + 2 * (first_std + last_std)
+            stds = np.random.uniform(
+                MIN_STD_FRAC * estimated_data_range,
+                MAX_STD_FRAC * estimated_data_range,
+                n_components - 2,
+            )
+            stds = np.concatenate([[first_std], stds, [last_std]])
+            means_order = np.argsort(means)
+            means = means[means_order]
+            stds = stds[means_order]
+            weights = weights[means_order]
+            # TODO: replace magic numbers with a constant/method in Problem class
+            if weights.min() < 0.03:
+                if log:
+                    print("weights too small")
+                continue
+            generated_data = generate_gaussian_mixture_data(
+                means, stds, weights, n_samples=n_components * N_SAMPLES_PER_COMPONENT
+            )
+            data_range = np.abs(np.max(generated_data) - np.min(generated_data))
+            if stds.max() > 0.1 * data_range:
+                if log:
+                    print("stds too large")
+                continue
+            if stds.min() < 0.001 * data_range:
+                if log:
+                    print("stds too small")
+                continue
+            problem = GaussianMixtureProblem(generated_data, n_components)
+            overlap = problem.overlap_error_by_density(means, stds, weights)
+            if overlap > max_overlap_value:
+                if log:
+                    print("overlap too large")
+                continue
+            if overlap < min_overlap_value:
+                if log:
+                    print("overlap too small")
+                continue
+            yield np.concatenate([weights, stds, means]), generated_data
+            break
+
+    return mixture_parameters, generated_datasets
+
+
+def generate_difficult_mixture_parameters(
+    n_mixtures: int,
+):
+    for idx in range(n_mixtures):
+        while True:
+            random.seed(idx)
+            np.random.seed(idx)
+            means = [0.0, 1.0]
+            # At least one std dev should be very small
+            stds = np.random.uniform(0.001, 0.005, 2)
+            weights = sample_weights(2)
+            generated_data = generate_gaussian_mixture_data(means, stds, weights, n_samples=2 * N_SAMPLES_PER_COMPONENT)
+            parameters = np.concatenate([weights, stds, means])
+            problem = GaussianMixtureProblem(generated_data, 2)
+            lower, upper = problem.get_bounds()
+            if np.any(parameters < lower) or np.any(parameters > upper):
+                continue
+            yield parameters, generated_data
+            break
diff --git a/distribution_optimization_py/utils.py b/distribution_optimization_py/utils.py
index fd5d081..bb4bf79 100644
--- a/distribution_optimization_py/utils.py
+++ b/distribution_optimization_py/utils.py
@@ -1,10 +1,58 @@
 import numpy as np
+from scipy import stats
+from scipy.integrate import quad
+from scipy.special import logsumexp
 from scipy.stats import norm
 
 DEFAULT_NR_OF_BINS = 10
+DEFAULT_N_SAMPLES = 100000
 
 
-def mixture_probability(
+def solution_to_params(solution: np.ndarray):
+    n_components = solution.shape[0] // 3
+    weights = solution[:n_components]
+    sds = solution[n_components : 2 * n_components]
+    means = solution[2 * n_components :]
+    means_order = np.argsort(means)
+    if not np.isclose(np.sum(weights), 1):
+        print("Weights do not sum to 1:", np.sum(weights))
+    weights = weights / np.sum(weights)
+    return weights[means_order], sds[means_order], means[means_order]
+
+
+def generate_gaussian_mixture_data(
+    means: np.ndarray,
+    sds: np.ndarray,
+    weights: np.ndarray,
+    n_samples: int | None = DEFAULT_N_SAMPLES,
+):
+    components = np.random.choice(len(weights), size=n_samples, p=weights)
+    samples = np.zeros(n_samples)
+    for component_idx in range(len(weights)):
+        mask = components == component_idx
+        n_component_samples = mask.sum()
+        if n_component_samples > 0:
+            samples[mask] = np.random.normal(
+                loc=means[component_idx],
+                scale=sds[component_idx],
+                size=n_component_samples,
+            )
+    return samples
+
+
+def get_log_likelihood(
+    data: np.ndarray,
+    means: np.ndarray,
+    sds: np.ndarray,
+    weights: np.ndarray,
+):
+    log_weights = np.log(weights)
+    log_probabilities = log_weights + norm.logpdf(data[:, np.newaxis], means, sds)
+    log_likelihood_values = logsumexp(log_probabilities, axis=1)
+    return log_likelihood_values
+
+
+def mixture_probabilities(
     x: np.ndarray,
     means: np.ndarray,
     sds: np.ndarray,
@@ -57,3 +105,64 @@ def optimal_no_bins(
             DEFAULT_NR_OF_BINS,
         )
     return DEFAULT_NR_OF_BINS
+
+
+def mann_wald_number_of_bins(data: np.ndarray) -> int:
+    """
+    Mann, H. B., & Wald, A. (1942). On the choice of the number of class intervals in the application of the chi square test.
+    """
+    n = len(data)
+    # Find c value by solving the integral equation:
+    # 1/sqrt(2π) ∫[c,∞] exp(-x²/2)dx = alpha
+    # This is equivalent to finding c where P(X > c) = alpha for X ~ N(0,1)
+    alpha = 0.05
+    c = stats.norm.ppf(1 - alpha)
+
+    k = 4 * np.power(2 * (n - 1) ** 2 / c**2, 1 / 5)
+    number_of_bins = int(np.round(k))
+    return max(DEFAULT_NR_OF_BINS, number_of_bins)
+
+
+def max_chi2_number_of_bins(data: np.ndarray) -> int:
+    min_count_per_bin = 5
+    return len(data) // min_count_per_bin
+
+
+def kl_div(solution1: np.ndarray, solution2: np.ndarray, n_samples: int = DEFAULT_N_SAMPLES):
+    weights1, sds1, means1 = solution_to_params(solution1)
+    weights2, sds2, means2 = solution_to_params(solution2)
+    samples = generate_gaussian_mixture_data(
+        means=means1,
+        sds=sds1,
+        weights=weights1,
+        n_samples=n_samples,
+    )
+    log_p = get_log_likelihood(data=samples, means=means1, sds=sds1, weights=weights1)
+    log_q = get_log_likelihood(data=samples, means=means2, sds=sds2, weights=weights2)
+    return np.mean(log_p) - np.mean(log_q)
+
+
+def js_div(solution1: np.ndarray, solution2: np.ndarray, n_samples: int = DEFAULT_N_SAMPLES):
+    weights1, sds1, means1 = solution_to_params(solution1)
+    weights2, sds2, means2 = solution_to_params(solution2)
+    samples1 = generate_gaussian_mixture_data(
+        means=means1,
+        sds=sds1,
+        weights=weights1,
+        n_samples=n_samples,
+    )
+    log_p1 = get_log_likelihood(data=samples1, means=means1, sds=sds1, weights=weights1)
+    log_q1 = get_log_likelihood(data=samples1, means=means2, sds=sds2, weights=weights2)
+    log_mix1 = np.logaddexp(log_p1, log_q1) - np.log(2)
+
+    samples2 = generate_gaussian_mixture_data(
+        means=means2,
+        sds=sds2,
+        weights=weights2,
+        n_samples=n_samples,
+    )
+    log_p2 = get_log_likelihood(data=samples2, means=means1, sds=sds1, weights=weights1)
+    log_q2 = get_log_likelihood(data=samples2, means=means2, sds=sds2, weights=weights2)
+    log_mix2 = np.logaddexp(log_p2, log_q2) - np.log(2)
+
+    return (np.mean(log_p1 - log_mix1) + np.mean(log_q2 - log_mix2)) / 2
diff --git a/images/algorithm_win_matrix.eps b/images/algorithm_win_matrix.eps
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+% path painting operators
+/S { sCol stroke } def
+/Sf { fCol stroke } def
+/f { fCol fill } def
+/f* { fCol eofill } def
+% clipping operators
+/W { clip newpath } def
+/W* { eoclip newpath } def
+/Ws { strokepath clip newpath } def
+% text state operators
+/Tc { /pdfCharSpacing exch def } def
+/Tf { dup /pdfFontSize exch def
+      dup pdfHorizScaling mul exch matrix scale
+      pdfTextMat matrix concatmatrix dup 4 0 put dup 5 0 put
+      exch findfont exch makefont setfont } def
+/Tr { /pdfTextRender exch def } def
+/Tp { /pdfPatternCS exch def } def
+/Ts { /pdfTextRise exch def } def
+/Tw { /pdfWordSpacing exch def } def
+/Tz { /pdfHorizScaling exch def } def
+% text positioning operators
+/Td { pdfTextMat transform moveto } def
+/Tm { /pdfTextMat exch def } def
+% text string operators
+/xyshow where {
+  pop
+  /xyshow2 {
+    dup length array
+    0 2 2 index length 1 sub {
+      2 index 1 index 2 copy get 3 1 roll 1 add get
+      pdfTextMat dtransform
+      4 2 roll 2 copy 6 5 roll put 1 add 3 1 roll dup 4 2 roll put
+    } for
+    exch pop
+    xyshow
+  } def
+}{
+  /xyshow2 {
+    currentfont /FontType get 0 eq {
+      0 2 3 index length 1 sub {
+        currentpoint 4 index 3 index 2 getinterval show moveto
+        2 copy get 2 index 3 2 roll 1 add get
+        pdfTextMat dtransform rmoveto
+      } for
+    } {
+      0 1 3 index length 1 sub {
+        currentpoint 4 index 3 index 1 getinterval show moveto
+        2 copy 2 mul get 2 index 3 2 roll 2 mul 1 add get
+        pdfTextMat dtransform rmoveto
+      } for
+    } ifelse
+    pop pop
+  } def
+} ifelse
+/cshow where {
+  pop
+  /xycp {
+    0 3 2 roll
+    {
+      pop pop currentpoint 3 2 roll
+      1 string dup 0 4 3 roll put false charpath moveto
+      2 copy get 2 index 2 index 1 add get
+      pdfTextMat dtransform rmoveto
+      2 add
+    } exch cshow
+    pop pop
+  } def
+}{
+  /xycp {
+    currentfont /FontType get 0 eq {
+      0 2 3 index length 1 sub {
+        currentpoint 4 index 3 index 2 getinterval false charpath moveto
+        2 copy get 2 index 3 2 roll 1 add get
+        pdfTextMat dtransform rmoveto
+      } for
+    } {
+      0 1 3 index length 1 sub {
+        currentpoint 4 index 3 index 1 getinterval false charpath moveto
+        2 copy 2 mul get 2 index 3 2 roll 2 mul 1 add get
+        pdfTextMat dtransform rmoveto
+      } for
+    } ifelse
+    pop pop
+  } def
+} ifelse
+/Tj {
+  fCol
+  0 pdfTextRise pdfTextMat dtransform rmoveto
+  currentpoint 4 2 roll
+  pdfTextRender 1 and 0 eq {
+    2 copy xyshow2
+  } if
+  pdfTextRender 3 and dup 1 eq exch 2 eq or {
+    3 index 3 index moveto
+    2 copy
+    currentfont /FontType get 3 eq { fCol } { sCol } ifelse
+    xycp currentpoint stroke moveto
+  } if
+  pdfTextRender 4 and 0 ne {
+    4 2 roll moveto xycp
+    /pdfTextClipPath [ pdfTextClipPath aload pop
+      {/moveto cvx}
+      {/lineto cvx}
+      {/curveto cvx}
+      {/closepath cvx}
+    pathforall ] def
+    currentpoint newpath moveto
+  } {
+    pop pop pop pop
+  } ifelse
+  0 pdfTextRise neg pdfTextMat dtransform rmoveto
+} def
+/TJm { 0.001 mul pdfFontSize mul pdfHorizScaling mul neg 0
+       pdfTextMat dtransform rmoveto } def
+/TJmV { 0.001 mul pdfFontSize mul neg 0 exch
+        pdfTextMat dtransform rmoveto } def
+/Tclip { pdfTextClipPath cvx exec clip newpath
+         /pdfTextClipPath [] def } def
+/Tclip* { pdfTextClipPath cvx exec eoclip newpath
+         /pdfTextClipPath [] def } def
+% Level 2/3 image operators
+/pdfImBuf 100 string def
+/pdfImStr {
+  2 copy exch length lt {
+    2 copy get exch 1 add exch
+  } {
+    ()
+  } ifelse
+} def
+/skipEOD {
+  { currentfile pdfImBuf readline
+    not { pop exit } if
+    (%-EOD-) eq { exit } if } loop
+} def
+/pdfIm { image skipEOD } def
+/pdfImM { fCol imagemask skipEOD } def
+/pr { 2 index 2 index 3 2 roll putinterval 4 add } def
+/pdfImClip {
+  gsave
+  0 2 4 index length 1 sub {
+    dup 4 index exch 2 copy
+    get 5 index div put
+    1 add 3 index exch 2 copy
+    get 3 index div put
+  } for
+  pop pop rectclip
+} def
+/pdfImClipEnd { grestore } def
+% shading operators
+/colordelta {
+  false 0 1 3 index length 1 sub {
+    dup 4 index exch get 3 index 3 2 roll get sub abs 0.004 gt {
+      pop true
+    } if
+  } for
+  exch pop exch pop
+} def
+/funcCol { func n array astore } def
+/funcSH {
+  dup 0 eq {
+    true
+  } {
+    dup 6 eq {
+      false
+    } {
+      4 index 4 index funcCol dup
+      6 index 4 index funcCol dup
+      3 1 roll colordelta 3 1 roll
+      5 index 5 index funcCol dup
+      3 1 roll colordelta 3 1 roll
+      6 index 8 index funcCol dup
+      3 1 roll colordelta 3 1 roll
+      colordelta or or or
+    } ifelse
+  } ifelse
+  {
+    1 add
+    4 index 3 index add 0.5 mul exch 4 index 3 index add 0.5 mul exch
+    6 index 6 index 4 index 4 index 4 index funcSH
+    2 index 6 index 6 index 4 index 4 index funcSH
+    6 index 2 index 4 index 6 index 4 index funcSH
+    5 3 roll 3 2 roll funcSH pop pop
+  } {
+    pop 3 index 2 index add 0.5 mul 3 index  2 index add 0.5 mul
+    funcCol sc
+    dup 4 index exch mat transform m
+    3 index 3 index mat transform l
+    1 index 3 index mat transform l
+    mat transform l pop pop h f*
+  } ifelse
+} def
+/axialCol {
+  dup 0 lt {
+    pop t0
+  } {
+    dup 1 gt {
+      pop t1
+    } {
+      dt mul t0 add
+    } ifelse
+  } ifelse
+  func n array astore
+} def
+/axialSH {
+  dup 0 eq {
+    true
+  } {
+    dup 8 eq {
+      false
+    } {
+      2 index axialCol 2 index axialCol colordelta
+    } ifelse
+  } ifelse
+  {
+    1 add 3 1 roll 2 copy add 0.5 mul
+    dup 4 3 roll exch 4 index axialSH
+    exch 3 2 roll axialSH
+  } {
+    pop 2 copy add 0.5 mul
+    axialCol sc
+    exch dup dx mul x0 add exch dy mul y0 add
+    3 2 roll dup dx mul x0 add exch dy mul y0 add
+    dx abs dy abs ge {
+      2 copy yMin sub dy mul dx div add yMin m
+      yMax sub dy mul dx div add yMax l
+      2 copy yMax sub dy mul dx div add yMax l
+      yMin sub dy mul dx div add yMin l
+      h f*
+    } {
+      exch 2 copy xMin sub dx mul dy div add xMin exch m
+      xMax sub dx mul dy div add xMax exch l
+      exch 2 copy xMax sub dx mul dy div add xMax exch l
+      xMin sub dx mul dy div add xMin exch l
+      h f*
+    } ifelse
+  } ifelse
+} def
+/radialCol {
+  dup t0 lt {
+    pop t0
+  } {
+    dup t1 gt {
+      pop t1
+    } if
+  } ifelse
+  func n array astore
+} def
+/radialSH {
+  dup 0 eq {
+    true
+  } {
+    dup 8 eq {
+      false
+    } {
+      2 index dt mul t0 add radialCol
+      2 index dt mul t0 add radialCol colordelta
+    } ifelse
+  } ifelse
+  {
+    1 add 3 1 roll 2 copy add 0.5 mul
+    dup 4 3 roll exch 4 index radialSH
+    exch 3 2 roll radialSH
+  } {
+    pop 2 copy add 0.5 mul dt mul t0 add
+    radialCol sc
+    encl {
+      exch dup dx mul x0 add exch dup dy mul y0 add exch dr mul r0 add
+      0 360 arc h
+      dup dx mul x0 add exch dup dy mul y0 add exch dr mul r0 add
+      360 0 arcn h f
+    } {
+      2 copy
+      dup dx mul x0 add exch dup dy mul y0 add exch dr mul r0 add
+      a1 a2 arcn
+      dup dx mul x0 add exch dup dy mul y0 add exch dr mul r0 add
+      a2 a1 arcn h
+      dup dx mul x0 add exch dup dy mul y0 add exch dr mul r0 add
+      a1 a2 arc
+      dup dx mul x0 add exch dup dy mul y0 add exch dr mul r0 add
+      a2 a1 arc h f
+    } ifelse
+  } ifelse
+} def
+end
+%%EndResource
+%%EndProlog
+%%BeginSetup
+xpdf begin
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+    pdfStrokeCS setcolorspace
+    pdfStroke aload pop pdfStrokeXform setcolor
+    pdfStrokeOP setoverprint
+    /pdfLastStroke true def /pdfLastFill false def
+  } if
+} def
+% build a font
+/pdfMakeFont {
+  4 3 roll findfont
+  4 2 roll matrix scale makefont
+  dup length dict begin
+    { 1 index /FID ne { def } { pop pop } ifelse } forall
+    /Encoding exch def
+    currentdict
+  end
+  definefont pop
+} def
+/pdfMakeFont16 {
+  exch findfont
+  dup length dict begin
+    { 1 index /FID ne { def } { pop pop } ifelse } forall
+    /WMode exch def
+    currentdict
+  end
+  definefont pop
+} def
+% graphics state operators
+/q { gsave pdfDictSize dict begin } def
+/Q {
+  end grestore
+  /pdfLastFill where {
+    pop
+    pdfLastFill {
+      pdfFillOP setoverprint
+    } {
+      pdfStrokeOP setoverprint
+    } ifelse
+  } if
+} def
+/cm { concat } def
+/d { setdash } def
+/i { setflat } def
+/j { setlinejoin } def
+/J { setlinecap } def
+/M { setmiterlimit } def
+/w { setlinewidth } def
+% path segment operators
+/m { moveto } def
+/l { lineto } def
+/c { curveto } def
+/re { 4 2 roll moveto 1 index 0 rlineto 0 exch rlineto
+      neg 0 rlineto closepath } def
+/h { closepath } def
+% path painting operators
+/S { sCol stroke } def
+/Sf { fCol stroke } def
+/f { fCol fill } def
+/f* { fCol eofill } def
+% clipping operators
+/W { clip newpath } def
+/W* { eoclip newpath } def
+/Ws { strokepath clip newpath } def
+% text state operators
+/Tc { /pdfCharSpacing exch def } def
+/Tf { dup /pdfFontSize exch def
+      dup pdfHorizScaling mul exch matrix scale
+      pdfTextMat matrix concatmatrix dup 4 0 put dup 5 0 put
+      exch findfont exch makefont setfont } def
+/Tr { /pdfTextRender exch def } def
+/Tp { /pdfPatternCS exch def } def
+/Ts { /pdfTextRise exch def } def
+/Tw { /pdfWordSpacing exch def } def
+/Tz { /pdfHorizScaling exch def } def
+% text positioning operators
+/Td { pdfTextMat transform moveto } def
+/Tm { /pdfTextMat exch def } def
+% text string operators
+/xyshow where {
+  pop
+  /xyshow2 {
+    dup length array
+    0 2 2 index length 1 sub {
+      2 index 1 index 2 copy get 3 1 roll 1 add get
+      pdfTextMat dtransform
+      4 2 roll 2 copy 6 5 roll put 1 add 3 1 roll dup 4 2 roll put
+    } for
+    exch pop
+    xyshow
+  } def
+}{
+  /xyshow2 {
+    currentfont /FontType get 0 eq {
+      0 2 3 index length 1 sub {
+        currentpoint 4 index 3 index 2 getinterval show moveto
+        2 copy get 2 index 3 2 roll 1 add get
+        pdfTextMat dtransform rmoveto
+      } for
+    } {
+      0 1 3 index length 1 sub {
+        currentpoint 4 index 3 index 1 getinterval show moveto
+        2 copy 2 mul get 2 index 3 2 roll 2 mul 1 add get
+        pdfTextMat dtransform rmoveto
+      } for
+    } ifelse
+    pop pop
+  } def
+} ifelse
+/cshow where {
+  pop
+  /xycp {
+    0 3 2 roll
+    {
+      pop pop currentpoint 3 2 roll
+      1 string dup 0 4 3 roll put false charpath moveto
+      2 copy get 2 index 2 index 1 add get
+      pdfTextMat dtransform rmoveto
+      2 add
+    } exch cshow
+    pop pop
+  } def
+}{
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+    currentfont /FontType get 0 eq {
+      0 2 3 index length 1 sub {
+        currentpoint 4 index 3 index 2 getinterval false charpath moveto
+        2 copy get 2 index 3 2 roll 1 add get
+        pdfTextMat dtransform rmoveto
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+      0 1 3 index length 1 sub {
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+        2 copy 2 mul get 2 index 3 2 roll 2 mul 1 add get
+        pdfTextMat dtransform rmoveto
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+  } def
+} ifelse
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+  currentpoint 4 2 roll
+  pdfTextRender 1 and 0 eq {
+    2 copy xyshow2
+  } if
+  pdfTextRender 3 and dup 1 eq exch 2 eq or {
+    3 index 3 index moveto
+    2 copy
+    currentfont /FontType get 3 eq { fCol } { sCol } ifelse
+    xycp currentpoint stroke moveto
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+    4 2 roll moveto xycp
+    /pdfTextClipPath [ pdfTextClipPath aload pop
+      {/moveto cvx}
+      {/lineto cvx}
+      {/curveto cvx}
+      {/closepath cvx}
+    pathforall ] def
+    currentpoint newpath moveto
+  } {
+    pop pop pop pop
+  } ifelse
+  0 pdfTextRise neg pdfTextMat dtransform rmoveto
+} def
+/TJm { 0.001 mul pdfFontSize mul pdfHorizScaling mul neg 0
+       pdfTextMat dtransform rmoveto } def
+/TJmV { 0.001 mul pdfFontSize mul neg 0 exch
+        pdfTextMat dtransform rmoveto } def
+/Tclip { pdfTextClipPath cvx exec clip newpath
+         /pdfTextClipPath [] def } def
+/Tclip* { pdfTextClipPath cvx exec eoclip newpath
+         /pdfTextClipPath [] def } def
+% Level 2/3 image operators
+/pdfImBuf 100 string def
+/pdfImStr {
+  2 copy exch length lt {
+    2 copy get exch 1 add exch
+  } {
+    ()
+  } ifelse
+} def
+/skipEOD {
+  { currentfile pdfImBuf readline
+    not { pop exit } if
+    (%-EOD-) eq { exit } if } loop
+} def
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+/pr { 2 index 2 index 3 2 roll putinterval 4 add } def
+/pdfImClip {
+  gsave
+  0 2 4 index length 1 sub {
+    dup 4 index exch 2 copy
+    get 5 index div put
+    1 add 3 index exch 2 copy
+    get 3 index div put
+  } for
+  pop pop rectclip
+} def
+/pdfImClipEnd { grestore } def
+% shading operators
+/colordelta {
+  false 0 1 3 index length 1 sub {
+    dup 4 index exch get 3 index 3 2 roll get sub abs 0.004 gt {
+      pop true
+    } if
+  } for
+  exch pop exch pop
+} def
+/funcCol { func n array astore } def
+/funcSH {
+  dup 0 eq {
+    true
+  } {
+    dup 6 eq {
+      false
+    } {
+      4 index 4 index funcCol dup
+      6 index 4 index funcCol dup
+      3 1 roll colordelta 3 1 roll
+      5 index 5 index funcCol dup
+      3 1 roll colordelta 3 1 roll
+      6 index 8 index funcCol dup
+      3 1 roll colordelta 3 1 roll
+      colordelta or or or
+    } ifelse
+  } ifelse
+  {
+    1 add
+    4 index 3 index add 0.5 mul exch 4 index 3 index add 0.5 mul exch
+    6 index 6 index 4 index 4 index 4 index funcSH
+    2 index 6 index 6 index 4 index 4 index funcSH
+    6 index 2 index 4 index 6 index 4 index funcSH
+    5 3 roll 3 2 roll funcSH pop pop
+  } {
+    pop 3 index 2 index add 0.5 mul 3 index  2 index add 0.5 mul
+    funcCol sc
+    dup 4 index exch mat transform m
+    3 index 3 index mat transform l
+    1 index 3 index mat transform l
+    mat transform l pop pop h f*
+  } ifelse
+} def
+/axialCol {
+  dup 0 lt {
+    pop t0
+  } {
+    dup 1 gt {
+      pop t1
+    } {
+      dt mul t0 add
+    } ifelse
+  } ifelse
+  func n array astore
+} def
+/axialSH {
+  dup 0 eq {
+    true
+  } {
+    dup 8 eq {
+      false
+    } {
+      2 index axialCol 2 index axialCol colordelta
+    } ifelse
+  } ifelse
+  {
+    1 add 3 1 roll 2 copy add 0.5 mul
+    dup 4 3 roll exch 4 index axialSH
+    exch 3 2 roll axialSH
+  } {
+    pop 2 copy add 0.5 mul
+    axialCol sc
+    exch dup dx mul x0 add exch dy mul y0 add
+    3 2 roll dup dx mul x0 add exch dy mul y0 add
+    dx abs dy abs ge {
+      2 copy yMin sub dy mul dx div add yMin m
+      yMax sub dy mul dx div add yMax l
+      2 copy yMax sub dy mul dx div add yMax l
+      yMin sub dy mul dx div add yMin l
+      h f*
+    } {
+      exch 2 copy xMin sub dx mul dy div add xMin exch m
+      xMax sub dx mul dy div add xMax exch l
+      exch 2 copy xMax sub dx mul dy div add xMax exch l
+      xMin sub dx mul dy div add xMin exch l
+      h f*
+    } ifelse
+  } ifelse
+} def
+/radialCol {
+  dup t0 lt {
+    pop t0
+  } {
+    dup t1 gt {
+      pop t1
+    } if
+  } ifelse
+  func n array astore
+} def
+/radialSH {
+  dup 0 eq {
+    true
+  } {
+    dup 8 eq {
+      false
+    } {
+      2 index dt mul t0 add radialCol
+      2 index dt mul t0 add radialCol colordelta
+    } ifelse
+  } ifelse
+  {
+    1 add 3 1 roll 2 copy add 0.5 mul
+    dup 4 3 roll exch 4 index radialSH
+    exch 3 2 roll radialSH
+  } {
+    pop 2 copy add 0.5 mul dt mul t0 add
+    radialCol sc
+    encl {
+      exch dup dx mul x0 add exch dup dy mul y0 add exch dr mul r0 add
+      0 360 arc h
+      dup dx mul x0 add exch dup dy mul y0 add exch dr mul r0 add
+      360 0 arcn h f
+    } {
+      2 copy
+      dup dx mul x0 add exch dup dy mul y0 add exch dr mul r0 add
+      a1 a2 arcn
+      dup dx mul x0 add exch dup dy mul y0 add exch dr mul r0 add
+      a2 a1 arcn h
+      dup dx mul x0 add exch dup dy mul y0 add exch dr mul r0 add
+      a1 a2 arc
+      dup dx mul x0 add exch dup dy mul y0 add exch dr mul r0 add
+      a2 a1 arc h f
+    } ifelse
+  } ifelse
+} def
+end
+%%EndResource
+%%EndProlog
+%%BeginSetup
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diff --git a/images/comparison_plot.eps b/images/comparison_plot.eps
new file mode 100644
index 0000000..c6daa9e
--- /dev/null
+++ b/images/comparison_plot.eps
@@ -0,0 +1,19948 @@
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+  definefont pop
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+/Tf { dup /pdfFontSize exch def
+      dup pdfHorizScaling mul exch matrix scale
+      pdfTextMat matrix concatmatrix dup 4 0 put dup 5 0 put
+      exch findfont exch makefont setfont } def
+/Tr { /pdfTextRender exch def } def
+/Tp { /pdfPatternCS exch def } def
+/Ts { /pdfTextRise exch def } def
+/Tw { /pdfWordSpacing exch def } def
+/Tz { /pdfHorizScaling exch def } def
+% text positioning operators
+/Td { pdfTextMat transform moveto } def
+/Tm { /pdfTextMat exch def } def
+% text string operators
+/xyshow where {
+  pop
+  /xyshow2 {
+    dup length array
+    0 2 2 index length 1 sub {
+      2 index 1 index 2 copy get 3 1 roll 1 add get
+      pdfTextMat dtransform
+      4 2 roll 2 copy 6 5 roll put 1 add 3 1 roll dup 4 2 roll put
+    } for
+    exch pop
+    xyshow
+  } def
+}{
+  /xyshow2 {
+    currentfont /FontType get 0 eq {
+      0 2 3 index length 1 sub {
+        currentpoint 4 index 3 index 2 getinterval show moveto
+        2 copy get 2 index 3 2 roll 1 add get
+        pdfTextMat dtransform rmoveto
+      } for
+    } {
+      0 1 3 index length 1 sub {
+        currentpoint 4 index 3 index 1 getinterval show moveto
+        2 copy 2 mul get 2 index 3 2 roll 2 mul 1 add get
+        pdfTextMat dtransform rmoveto
+      } for
+    } ifelse
+    pop pop
+  } def
+} ifelse
+/cshow where {
+  pop
+  /xycp {
+    0 3 2 roll
+    {
+      pop pop currentpoint 3 2 roll
+      1 string dup 0 4 3 roll put false charpath moveto
+      2 copy get 2 index 2 index 1 add get
+      pdfTextMat dtransform rmoveto
+      2 add
+    } exch cshow
+    pop pop
+  } def
+}{
+  /xycp {
+    currentfont /FontType get 0 eq {
+      0 2 3 index length 1 sub {
+        currentpoint 4 index 3 index 2 getinterval false charpath moveto
+        2 copy get 2 index 3 2 roll 1 add get
+        pdfTextMat dtransform rmoveto
+      } for
+    } {
+      0 1 3 index length 1 sub {
+        currentpoint 4 index 3 index 1 getinterval false charpath moveto
+        2 copy 2 mul get 2 index 3 2 roll 2 mul 1 add get
+        pdfTextMat dtransform rmoveto
+      } for
+    } ifelse
+    pop pop
+  } def
+} ifelse
+/Tj {
+  fCol
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+  currentpoint 4 2 roll
+  pdfTextRender 1 and 0 eq {
+    2 copy xyshow2
+  } if
+  pdfTextRender 3 and dup 1 eq exch 2 eq or {
+    3 index 3 index moveto
+    2 copy
+    currentfont /FontType get 3 eq { fCol } { sCol } ifelse
+    xycp currentpoint stroke moveto
+  } if
+  pdfTextRender 4 and 0 ne {
+    4 2 roll moveto xycp
+    /pdfTextClipPath [ pdfTextClipPath aload pop
+      {/moveto cvx}
+      {/lineto cvx}
+      {/curveto cvx}
+      {/closepath cvx}
+    pathforall ] def
+    currentpoint newpath moveto
+  } {
+    pop pop pop pop
+  } ifelse
+  0 pdfTextRise neg pdfTextMat dtransform rmoveto
+} def
+/TJm { 0.001 mul pdfFontSize mul pdfHorizScaling mul neg 0
+       pdfTextMat dtransform rmoveto } def
+/TJmV { 0.001 mul pdfFontSize mul neg 0 exch
+        pdfTextMat dtransform rmoveto } def
+/Tclip { pdfTextClipPath cvx exec clip newpath
+         /pdfTextClipPath [] def } def
+/Tclip* { pdfTextClipPath cvx exec eoclip newpath
+         /pdfTextClipPath [] def } def
+% Level 2/3 image operators
+/pdfImBuf 100 string def
+/pdfImStr {
+  2 copy exch length lt {
+    2 copy get exch 1 add exch
+  } {
+    ()
+  } ifelse
+} def
+/skipEOD {
+  { currentfile pdfImBuf readline
+    not { pop exit } if
+    (%-EOD-) eq { exit } if } loop
+} def
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+/pdfImClip {
+  gsave
+  0 2 4 index length 1 sub {
+    dup 4 index exch 2 copy
+    get 5 index div put
+    1 add 3 index exch 2 copy
+    get 3 index div put
+  } for
+  pop pop rectclip
+} def
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+  false 0 1 3 index length 1 sub {
+    dup 4 index exch get 3 index 3 2 roll get sub abs 0.004 gt {
+      pop true
+    } if
+  } for
+  exch pop exch pop
+} def
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+  dup 0 eq {
+    true
+  } {
+    dup 6 eq {
+      false
+    } {
+      4 index 4 index funcCol dup
+      6 index 4 index funcCol dup
+      3 1 roll colordelta 3 1 roll
+      5 index 5 index funcCol dup
+      3 1 roll colordelta 3 1 roll
+      6 index 8 index funcCol dup
+      3 1 roll colordelta 3 1 roll
+      colordelta or or or
+    } ifelse
+  } ifelse
+  {
+    1 add
+    4 index 3 index add 0.5 mul exch 4 index 3 index add 0.5 mul exch
+    6 index 6 index 4 index 4 index 4 index funcSH
+    2 index 6 index 6 index 4 index 4 index funcSH
+    6 index 2 index 4 index 6 index 4 index funcSH
+    5 3 roll 3 2 roll funcSH pop pop
+  } {
+    pop 3 index 2 index add 0.5 mul 3 index  2 index add 0.5 mul
+    funcCol sc
+    dup 4 index exch mat transform m
+    3 index 3 index mat transform l
+    1 index 3 index mat transform l
+    mat transform l pop pop h f*
+  } ifelse
+} def
+/axialCol {
+  dup 0 lt {
+    pop t0
+  } {
+    dup 1 gt {
+      pop t1
+    } {
+      dt mul t0 add
+    } ifelse
+  } ifelse
+  func n array astore
+} def
+/axialSH {
+  dup 0 eq {
+    true
+  } {
+    dup 8 eq {
+      false
+    } {
+      2 index axialCol 2 index axialCol colordelta
+    } ifelse
+  } ifelse
+  {
+    1 add 3 1 roll 2 copy add 0.5 mul
+    dup 4 3 roll exch 4 index axialSH
+    exch 3 2 roll axialSH
+  } {
+    pop 2 copy add 0.5 mul
+    axialCol sc
+    exch dup dx mul x0 add exch dy mul y0 add
+    3 2 roll dup dx mul x0 add exch dy mul y0 add
+    dx abs dy abs ge {
+      2 copy yMin sub dy mul dx div add yMin m
+      yMax sub dy mul dx div add yMax l
+      2 copy yMax sub dy mul dx div add yMax l
+      yMin sub dy mul dx div add yMin l
+      h f*
+    } {
+      exch 2 copy xMin sub dx mul dy div add xMin exch m
+      xMax sub dx mul dy div add xMax exch l
+      exch 2 copy xMax sub dx mul dy div add xMax exch l
+      xMin sub dx mul dy div add xMin exch l
+      h f*
+    } ifelse
+  } ifelse
+} def
+/radialCol {
+  dup t0 lt {
+    pop t0
+  } {
+    dup t1 gt {
+      pop t1
+    } if
+  } ifelse
+  func n array astore
+} def
+/radialSH {
+  dup 0 eq {
+    true
+  } {
+    dup 8 eq {
+      false
+    } {
+      2 index dt mul t0 add radialCol
+      2 index dt mul t0 add radialCol colordelta
+    } ifelse
+  } ifelse
+  {
+    1 add 3 1 roll 2 copy add 0.5 mul
+    dup 4 3 roll exch 4 index radialSH
+    exch 3 2 roll radialSH
+  } {
+    pop 2 copy add 0.5 mul dt mul t0 add
+    radialCol sc
+    encl {
+      exch dup dx mul x0 add exch dup dy mul y0 add exch dr mul r0 add
+      0 360 arc h
+      dup dx mul x0 add exch dup dy mul y0 add exch dr mul r0 add
+      360 0 arcn h f
+    } {
+      2 copy
+      dup dx mul x0 add exch dup dy mul y0 add exch dr mul r0 add
+      a1 a2 arcn
+      dup dx mul x0 add exch dup dy mul y0 add exch dr mul r0 add
+      a2 a1 arcn h
+      dup dx mul x0 add exch dup dy mul y0 add exch dr mul r0 add
+      a1 a2 arc
+      dup dx mul x0 add exch dup dy mul y0 add exch dr mul r0 add
+      a2 a1 arc h f
+    } ifelse
+  } ifelse
+} def
+end
+%%EndResource
+%%EndProlog
+%%BeginSetup
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diff --git a/poetry.lock b/poetry.lock
index a6b6e03..fe69bf6 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand.
 
 [[package]]
 name = "alembic"
@@ -6,6 +6,7 @@ version = "1.13.1"
 description = "A database migration tool for SQLAlchemy."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "alembic-1.13.1-py3-none-any.whl", hash = "sha256:2edcc97bed0bd3272611ce3a98d98279e9c209e7186e43e75bbb1b2bdfdbcc43"},
     {file = "alembic-1.13.1.tar.gz", hash = "sha256:4932c8558bf68f2ee92b9bbcb8218671c627064d5b08939437af6d77dc05e595"},
@@ -17,7 +18,7 @@ SQLAlchemy = ">=1.3.0"
 typing-extensions = ">=4"
 
 [package.extras]
-tz = ["backports.zoneinfo"]
+tz = ["backports.zoneinfo ; python_version < \"3.9\""]
 
 [[package]]
 name = "antlr4-python3-runtime"
@@ -25,6 +26,7 @@ version = "4.9.3"
 description = "ANTLR 4.9.3 runtime for Python 3.7"
 optional = false
 python-versions = "*"
+groups = ["main"]
 files = [
     {file = "antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b"},
 ]
@@ -35,6 +37,8 @@ version = "0.1.3"
 description = "Disable App Nap on macOS >= 10.9"
 optional = false
 python-versions = "*"
+groups = ["dev"]
+markers = "platform_system == \"Darwin\""
 files = [
     {file = "appnope-0.1.3-py2.py3-none-any.whl", hash = "sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e"},
     {file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"},
@@ -46,6 +50,7 @@ version = "6.1.4"
 description = "Astronomy and astrophysics core library"
 optional = false
 python-versions = ">=3.10"
+groups = ["main"]
 files = [
     {file = "astropy-6.1.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:7954cdfea00445ed186431888ec8ab12d9e3adfdf316038c44009f57438e6389"},
     {file = "astropy-6.1.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c0298f0e0d57cc61915a4438d54e47b4517ff6c7c5144ae4679077cfb0826d0b"},
@@ -87,7 +92,7 @@ PyYAML = ">=3.13"
 
 [package.extras]
 all = ["asdf-astropy (>=0.3)", "astropy[recommended]", "astropy[typing]", "beautifulsoup4", "bleach", "bottleneck", "certifi", "dask[array]", "fsspec[http] (>=2023.4.0)", "h5py", "html5lib", "ipython (>=4.2)", "jplephem", "mpmath", "pandas", "pre-commit", "pyarrow (>=7.0.0)", "pytest (>=7.0)", "pytz", "s3fs (>=2023.4.0)", "sortedcontainers"]
-docs = ["Jinja2 (>=3.1.3)", "astropy[recommended]", "matplotlib (>=3.9.1)", "numpy (<2.0)", "pytest (>=7.0)", "sphinx", "sphinx-astropy[confv2] (>=1.9.1)", "sphinx-changelog (>=1.2.0)", "sphinx-design", "sphinxcontrib-globalsubs (>=0.1.1)", "tomli"]
+docs = ["Jinja2 (>=3.1.3)", "astropy[recommended]", "matplotlib (>=3.9.1)", "numpy (<2.0)", "pytest (>=7.0)", "sphinx", "sphinx-astropy[confv2] (>=1.9.1)", "sphinx-changelog (>=1.2.0)", "sphinx-design", "sphinxcontrib-globalsubs (>=0.1.1)", "tomli ; python_version < \"3.11\""]
 recommended = ["matplotlib (>=3.5.0,!=3.5.2)", "scipy (>=1.8)"]
 test = ["pytest (>=7.0)", "pytest-astropy (>=0.10)", "pytest-astropy-header (>=0.2.1)", "pytest-doctestplus (>=0.12)", "pytest-xdist", "threadpoolctl"]
 test-all = ["array-api-strict", "astropy[test]", "coverage[toml]", "ipython (>=4.2)", "objgraph", "sgp4 (>=2.3)", "skyfield (>=1.20)"]
@@ -99,6 +104,7 @@ version = "0.2024.10.7.0.32.46"
 description = "IERS Earth Rotation and Leap Second tables for the astropy core package"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "astropy_iers_data-0.2024.10.7.0.32.46-py3-none-any.whl", hash = "sha256:4b5ce3e3c8ac17632a28a35f4b847d8a8db084509f5f0db3238140dbb5149eae"},
     {file = "astropy_iers_data-0.2024.10.7.0.32.46.tar.gz", hash = "sha256:349ff09294f03112d9391a5ad660135bf2fcef7de411a52c20bcf8598fb029d1"},
@@ -114,6 +120,7 @@ version = "2.4.1"
 description = "Annotate AST trees with source code positions"
 optional = false
 python-versions = "*"
+groups = ["dev"]
 files = [
     {file = "asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24"},
     {file = "asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"},
@@ -123,8 +130,8 @@ files = [
 six = ">=1.12.0"
 
 [package.extras]
-astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
-test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
+astroid = ["astroid (>=1,<2) ; python_version < \"3\"", "astroid (>=2,<4) ; python_version >= \"3\""]
+test = ["astroid (>=1,<2) ; python_version < \"3\"", "astroid (>=2,<4) ; python_version >= \"3\"", "pytest"]
 
 [[package]]
 name = "bottle"
@@ -132,6 +139,7 @@ version = "0.12.25"
 description = "Fast and simple WSGI-framework for small web-applications."
 optional = false
 python-versions = "*"
+groups = ["dev"]
 files = [
     {file = "bottle-0.12.25-py3-none-any.whl", hash = "sha256:d6f15f9d422670b7c073d63bd8d287b135388da187a0f3e3c19293626ce034ea"},
     {file = "bottle-0.12.25.tar.gz", hash = "sha256:e1a9c94970ae6d710b3fb4526294dfeb86f2cb4a81eff3a4b98dc40fb0e5e021"},
@@ -143,6 +151,8 @@ version = "1.16.0"
 description = "Foreign Function Interface for Python calling C code."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "implementation_name == \"pypy\""
 files = [
     {file = "cffi-1.16.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:6b3d6606d369fc1da4fd8c357d026317fbb9c9b75d36dc16e90e84c26854b088"},
     {file = "cffi-1.16.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ac0f5edd2360eea2f1daa9e26a41db02dd4b0451b48f7c318e217ee092a213e9"},
@@ -207,6 +217,7 @@ version = "3.4.0"
 description = "Validate configuration and produce human readable error messages."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "cfgv-3.4.0-py2.py3-none-any.whl", hash = "sha256:b7265b1f29fd3316bfcd2b330d63d024f2bfd8bcb8b0272f8e19a504856c48f9"},
     {file = "cfgv-3.4.0.tar.gz", hash = "sha256:e52591d4c5f5dead8e0f673fb16db7949d2cfb3f7da4582893288f0ded8fe560"},
@@ -218,6 +229,7 @@ version = "8.1.7"
 description = "Composable command line interface toolkit"
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28"},
     {file = "click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"},
@@ -232,6 +244,7 @@ version = "3.0.0"
 description = "Pickler class to extend the standard pickle.Pickler functionality"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "cloudpickle-3.0.0-py3-none-any.whl", hash = "sha256:246ee7d0c295602a036e86369c77fecda4ab17b506496730f2f576d9016fd9c7"},
     {file = "cloudpickle-3.0.0.tar.gz", hash = "sha256:996d9a482c6fb4f33c1a35335cf8afd065d2a56e973270364840712d9131a882"},
@@ -243,6 +256,7 @@ version = "3.3.0"
 description = "CMA-ES, Covariance Matrix Adaptation Evolution Strategy for non-linear numerical optimization in Python"
 optional = false
 python-versions = "*"
+groups = ["main"]
 files = [
     {file = "cma-3.3.0-py3-none-any.whl", hash = "sha256:5cc571b1e2068fcf1c538be36f8f3a870107456fed22ce81c1345a96329e61db"},
     {file = "cma-3.3.0.tar.gz", hash = "sha256:b748b8e03f4e7ae816157d7b9bb2fc6b1fb2fee1d5fd3399329b646bb75861ec"},
@@ -261,10 +275,12 @@ version = "0.4.6"
 description = "Cross-platform colored terminal text."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
+groups = ["main", "dev"]
 files = [
     {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
+markers = {main = "platform_system == \"Windows\"", dev = "sys_platform == \"win32\" or platform_system == \"Windows\""}
 
 [[package]]
 name = "colorlog"
@@ -272,6 +288,7 @@ version = "6.8.2"
 description = "Add colours to the output of Python's logging module."
 optional = false
 python-versions = ">=3.6"
+groups = ["dev"]
 files = [
     {file = "colorlog-6.8.2-py3-none-any.whl", hash = "sha256:4dcbb62368e2800cb3c5abd348da7e53f6c362dda502ec27c560b2e58a66bd33"},
     {file = "colorlog-6.8.2.tar.gz", hash = "sha256:3e3e079a41feb5a1b64f978b5ea4f46040a94f11f0e8bbb8261e3dbbeca64d44"},
@@ -289,6 +306,7 @@ version = "0.2.1"
 description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "comm-0.2.1-py3-none-any.whl", hash = "sha256:87928485c0dfc0e7976fd89fc1e187023cf587e7c353e4a9b417555b44adf021"},
     {file = "comm-0.2.1.tar.gz", hash = "sha256:0bc91edae1344d39d3661dcbc36937181fdaddb304790458f8b044dbc064b89a"},
@@ -306,6 +324,7 @@ version = "1.2.0"
 description = "Python library for calculating contours of 2D quadrilateral grids"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "contourpy-1.2.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0274c1cb63625972c0c007ab14dd9ba9e199c36ae1a231ce45d725cbcbfd10a8"},
     {file = "contourpy-1.2.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ab459a1cbbf18e8698399c595a01f6dcc5c138220ca3ea9e7e6126232d102bb4"},
@@ -369,6 +388,7 @@ version = "7.4.3"
 description = "Code coverage measurement for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "coverage-7.4.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:8580b827d4746d47294c0e0b92854c85a92c2227927433998f0d3320ae8a71b6"},
     {file = "coverage-7.4.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:718187eeb9849fc6cc23e0d9b092bc2348821c5e1a901c9f8975df0bc785bfd4"},
@@ -428,7 +448,7 @@ files = [
 tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.11.0a6\" and extra == \"toml\""}
 
 [package.extras]
-toml = ["tomli"]
+toml = ["tomli ; python_full_version <= \"3.11.0a6\""]
 
 [[package]]
 name = "cycler"
@@ -436,6 +456,7 @@ version = "0.12.1"
 description = "Composable style cycles"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30"},
     {file = "cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c"},
@@ -451,6 +472,7 @@ version = "2024.8.2"
 description = "Parallel PyData with Task Scheduling"
 optional = false
 python-versions = ">=3.10"
+groups = ["main"]
 files = [
     {file = "dask-2024.8.2-py3-none-any.whl", hash = "sha256:484c317ee870140dbeccc759a91fd98821bba98d71a43eda7856fc9e859b03a1"},
     {file = "dask-2024.8.2.tar.gz", hash = "sha256:1d05e09166ac7927a54401e998aa1599e86aac666edbdaeb3096bf4b957029e0"},
@@ -480,6 +502,7 @@ version = "1.8.0"
 description = "An implementation of the Debug Adapter Protocol for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "debugpy-1.8.0-cp310-cp310-macosx_11_0_x86_64.whl", hash = "sha256:7fb95ca78f7ac43393cd0e0f2b6deda438ec7c5e47fa5d38553340897d2fbdfb"},
     {file = "debugpy-1.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ef9ab7df0b9a42ed9c878afd3eaaff471fce3fa73df96022e1f5c9f8f8c87ada"},
@@ -507,6 +530,7 @@ version = "5.1.1"
 description = "Decorators for Humans"
 optional = false
 python-versions = ">=3.5"
+groups = ["dev"]
 files = [
     {file = "decorator-5.1.1-py3-none-any.whl", hash = "sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186"},
     {file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
@@ -518,6 +542,7 @@ version = "0.3.8"
 description = "serialize all of Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "dill-0.3.8-py3-none-any.whl", hash = "sha256:c36ca9ffb54365bdd2f8eb3eff7d2a21237f8452b57ace88b1ac615b7e815bd7"},
     {file = "dill-0.3.8.tar.gz", hash = "sha256:3ebe3c479ad625c4553aca177444d89b486b1d84982eeacded644afc0cf797ca"},
@@ -533,6 +558,7 @@ version = "0.3.8"
 description = "Distribution utilities"
 optional = false
 python-versions = "*"
+groups = ["dev"]
 files = [
     {file = "distlib-0.3.8-py2.py3-none-any.whl", hash = "sha256:034db59a0b96f8ca18035f36290806a9a6e6bd9d1ff91e45a7f172eb17e51784"},
     {file = "distlib-0.3.8.tar.gz", hash = "sha256:1530ea13e350031b6312d8580ddb6b27a104275a31106523b8f123787f494f64"},
@@ -544,6 +570,7 @@ version = "2024.8.2"
 description = "Distributed scheduler for Dask"
 optional = false
 python-versions = ">=3.10"
+groups = ["main"]
 files = [
     {file = "distributed-2024.8.2-py3-none-any.whl", hash = "sha256:0c75888bd1208e59990a373ea139eed8fba3927fff6ea1e56e43536d8fd4f105"},
     {file = "distributed-2024.8.2.tar.gz", hash = "sha256:3fceae46d6e7b771c15cc1e2c8349f2900f003bb9846c471c14c19a0c0025197"},
@@ -572,6 +599,7 @@ version = "0.6.2"
 description = "Pythonic argument parser, that will make you smile"
 optional = false
 python-versions = "*"
+groups = ["main"]
 files = [
     {file = "docopt-0.6.2.tar.gz", hash = "sha256:49b3a825280bd66b3aa83585ef59c4a8c82f2c8a522dbe754a8bc8d08c85c491"},
 ]
@@ -582,6 +610,8 @@ version = "1.2.0"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version < \"3.11\""
 files = [
     {file = "exceptiongroup-1.2.0-py3-none-any.whl", hash = "sha256:4bfd3996ac73b41e9b9628b04e079f193850720ea5945fc96a08633c66912f14"},
     {file = "exceptiongroup-1.2.0.tar.gz", hash = "sha256:91f5c769735f051a4290d52edd0858999b57e5876e9f85937691bd4c9fa3ed68"},
@@ -596,13 +626,14 @@ version = "2.0.1"
 description = "Get the currently executing AST node of a frame, and other information"
 optional = false
 python-versions = ">=3.5"
+groups = ["dev"]
 files = [
     {file = "executing-2.0.1-py2.py3-none-any.whl", hash = "sha256:eac49ca94516ccc753f9fb5ce82603156e590b27525a8bc32cce8ae302eb61bc"},
     {file = "executing-2.0.1.tar.gz", hash = "sha256:35afe2ce3affba8ee97f2d69927fa823b08b472b7b994e36a52a964b93d16147"},
 ]
 
 [package.extras]
-tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich"]
+tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich ; python_version >= \"3.11\""]
 
 [[package]]
 name = "filelock"
@@ -610,6 +641,7 @@ version = "3.13.1"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "filelock-3.13.1-py3-none-any.whl", hash = "sha256:57dbda9b35157b05fb3e58ee91448612eb674172fab98ee235ccb0b5bee19a1c"},
     {file = "filelock-3.13.1.tar.gz", hash = "sha256:521f5f56c50f8426f5e03ad3b281b490a87ef15bc6c526f168290f0c7148d44e"},
@@ -618,7 +650,7 @@ files = [
 [package.extras]
 docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.24)"]
 testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
-typing = ["typing-extensions (>=4.8)"]
+typing = ["typing-extensions (>=4.8) ; python_version < \"3.11\""]
 
 [[package]]
 name = "fonttools"
@@ -626,6 +658,7 @@ version = "4.47.2"
 description = "Tools to manipulate font files"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "fonttools-4.47.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:3b629108351d25512d4ea1a8393a2dba325b7b7d7308116b605ea3f8e1be88df"},
     {file = "fonttools-4.47.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c19044256c44fe299d9a73456aabee4b4d06c6b930287be93b533b4737d70aa1"},
@@ -672,18 +705,18 @@ files = [
 ]
 
 [package.extras]
-all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "pycairo", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.1.0)", "xattr", "zopfli (>=0.1.4)"]
+all = ["brotli (>=1.0.1) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\"", "fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres ; platform_python_implementation == \"PyPy\"", "pycairo", "scipy ; platform_python_implementation != \"PyPy\"", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.1.0) ; python_version <= \"3.12\"", "xattr ; sys_platform == \"darwin\"", "zopfli (>=0.1.4)"]
 graphite = ["lz4 (>=1.7.4.2)"]
-interpolatable = ["munkres", "pycairo", "scipy"]
+interpolatable = ["munkres ; platform_python_implementation == \"PyPy\"", "pycairo", "scipy ; platform_python_implementation != \"PyPy\""]
 lxml = ["lxml (>=4.0,<5)"]
 pathops = ["skia-pathops (>=0.5.0)"]
 plot = ["matplotlib"]
 repacker = ["uharfbuzz (>=0.23.0)"]
 symfont = ["sympy"]
-type1 = ["xattr"]
+type1 = ["xattr ; sys_platform == \"darwin\""]
 ufo = ["fs (>=2.2.0,<3)"]
-unicode = ["unicodedata2 (>=15.1.0)"]
-woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
+unicode = ["unicodedata2 (>=15.1.0) ; python_version <= \"3.12\""]
+woff = ["brotli (>=1.0.1) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\"", "zopfli (>=0.1.4)"]
 
 [[package]]
 name = "fsspec"
@@ -691,6 +724,7 @@ version = "2024.9.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "fsspec-2024.9.0-py3-none-any.whl", hash = "sha256:a0947d552d8a6efa72cc2c730b12c41d043509156966cca4fb157b0f2a0c574b"},
     {file = "fsspec-2024.9.0.tar.gz", hash = "sha256:4b0afb90c2f21832df142f292649035d80b421f60a9e1c027802e5a0da2b04e8"},
@@ -730,6 +764,8 @@ version = "3.0.3"
 description = "Lightweight in-process concurrent programming"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\""
 files = [
     {file = "greenlet-3.0.3-cp310-cp310-macosx_11_0_universal2.whl", hash = "sha256:9da2bd29ed9e4f15955dd1595ad7bc9320308a3b766ef7f837e23ad4b4aac31a"},
     {file = "greenlet-3.0.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d353cadd6083fdb056bb46ed07e4340b0869c305c8ca54ef9da3421acbdf6881"},
@@ -801,6 +837,7 @@ version = "2.5.35"
 description = "File identification library for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "identify-2.5.35-py2.py3-none-any.whl", hash = "sha256:c4de0081837b211594f8e877a6b4fad7ca32bbfc1a9307fdd61c28bfe923f13e"},
     {file = "identify-2.5.35.tar.gz", hash = "sha256:10a7ca245cfcd756a554a7288159f72ff105ad233c7c4b9c6f0f4d108f5f6791"},
@@ -815,6 +852,8 @@ version = "8.5.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version <= \"3.11\""
 files = [
     {file = "importlib_metadata-8.5.0-py3-none-any.whl", hash = "sha256:45e54197d28b7a7f1559e60b95e7c567032b602131fbd588f1497f47880aa68b"},
     {file = "importlib_metadata-8.5.0.tar.gz", hash = "sha256:71522656f0abace1d072b9e5481a48f07c138e00f079c38c8f883823f9c26bd7"},
@@ -824,12 +863,12 @@ files = [
 zipp = ">=3.20"
 
 [package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
 cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 enabler = ["pytest-enabler (>=2.2)"]
 perf = ["ipython"]
-test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
+test = ["flufl.flake8", "importlib-resources (>=1.3) ; python_version < \"3.9\"", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
 type = ["pytest-mypy"]
 
 [[package]]
@@ -838,6 +877,7 @@ version = "2.0.0"
 description = "brain-dead simple config-ini parsing"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"},
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
@@ -849,6 +889,7 @@ version = "6.29.0"
 description = "IPython Kernel for Jupyter"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "ipykernel-6.29.0-py3-none-any.whl", hash = "sha256:076663ca68492576f051e4af7720d33f34383e655f2be0d544c8b1c9de915b2f"},
     {file = "ipykernel-6.29.0.tar.gz", hash = "sha256:b5dd3013cab7b330df712891c96cd1ab868c27a7159e606f762015e9bf8ceb3f"},
@@ -882,6 +923,7 @@ version = "8.20.0"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.10"
+groups = ["dev"]
 files = [
     {file = "ipython-8.20.0-py3-none-any.whl", hash = "sha256:bc9716aad6f29f36c449e30821c9dd0c1c1a7b59ddcc26931685b87b4c569619"},
     {file = "ipython-8.20.0.tar.gz", hash = "sha256:2f21bd3fc1d51550c89ee3944ae04bbc7bc79e129ea0937da6e6c68bfdbf117a"},
@@ -918,6 +960,7 @@ version = "0.19.1"
 description = "An autocompletion tool for Python that can be used for text editors."
 optional = false
 python-versions = ">=3.6"
+groups = ["dev"]
 files = [
     {file = "jedi-0.19.1-py2.py3-none-any.whl", hash = "sha256:e983c654fe5c02867aef4cdfce5a2fbb4a50adc0af145f70504238f18ef5e7e0"},
     {file = "jedi-0.19.1.tar.gz", hash = "sha256:cf0496f3651bc65d7174ac1b7d043eff454892c708a87d1b683e57b569927ffd"},
@@ -937,6 +980,7 @@ version = "3.1.4"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "jinja2-3.1.4-py3-none-any.whl", hash = "sha256:bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d"},
     {file = "jinja2-3.1.4.tar.gz", hash = "sha256:4a3aee7acbbe7303aede8e9648d13b8bf88a429282aa6122a993f0ac800cb369"},
@@ -954,6 +998,7 @@ version = "1.3.2"
 description = "Lightweight pipelining with Python functions"
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
 files = [
     {file = "joblib-1.3.2-py3-none-any.whl", hash = "sha256:ef4331c65f239985f3f2220ecc87db222f08fd22097a3dd5698f693875f8cbb9"},
     {file = "joblib-1.3.2.tar.gz", hash = "sha256:92f865e621e17784e7955080b6d042489e3b8e294949cc44c6eac304f59772b1"},
@@ -965,6 +1010,7 @@ version = "8.6.0"
 description = "Jupyter protocol implementation and client libraries"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "jupyter_client-8.6.0-py3-none-any.whl", hash = "sha256:909c474dbe62582ae62b758bca86d6518c85234bdee2d908c778db6d72f39d99"},
     {file = "jupyter_client-8.6.0.tar.gz", hash = "sha256:0642244bb83b4764ae60d07e010e15f0e2d275ec4e918a8f7b80fbbef3ca60c7"},
@@ -979,7 +1025,7 @@ traitlets = ">=5.3"
 
 [package.extras]
 docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
-test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"]
+test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko ; sys_platform == \"win32\"", "pre-commit", "pytest", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"]
 
 [[package]]
 name = "jupyter-core"
@@ -987,6 +1033,7 @@ version = "5.7.1"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "jupyter_core-5.7.1-py3-none-any.whl", hash = "sha256:c65c82126453a723a2804aa52409930434598fd9d35091d63dfb919d2b765bb7"},
     {file = "jupyter_core-5.7.1.tar.gz", hash = "sha256:de61a9d7fc71240f688b2fb5ab659fbb56979458dc66a71decd098e03c79e218"},
@@ -1007,6 +1054,7 @@ version = "0.2.1"
 description = "Static image export for web-based visualization libraries with zero dependencies"
 optional = false
 python-versions = "*"
+groups = ["main"]
 files = [
     {file = "kaleido-0.2.1-py2.py3-none-macosx_10_11_x86_64.whl", hash = "sha256:ca6f73e7ff00aaebf2843f73f1d3bacde1930ef5041093fe76b83a15785049a7"},
     {file = "kaleido-0.2.1-py2.py3-none-macosx_11_0_arm64.whl", hash = "sha256:bb9a5d1f710357d5d432ee240ef6658a6d124c3e610935817b4b42da9c787c05"},
@@ -1022,6 +1070,7 @@ version = "1.4.5"
 description = "A fast implementation of the Cassowary constraint solver"
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "kiwisolver-1.4.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:05703cf211d585109fcd72207a31bb170a0f22144d68298dc5e61b3c946518af"},
     {file = "kiwisolver-1.4.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:146d14bebb7f1dc4d5fbf74f8a6cb15ac42baadee8912eb84ac0b3b2a3dc6ac3"},
@@ -1135,6 +1184,7 @@ version = "0.1.0"
 description = "Toolbox for fitness landscape analysis."
 optional = false
 python-versions = "^3.10"
+groups = ["main"]
 files = []
 develop = false
 
@@ -1158,6 +1208,7 @@ version = "0.8.1"
 description = "A general purpose Library for Evolutionary Algorithms in Python."
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "leap_ec-0.8.1-py3-none-any.whl", hash = "sha256:2e295040fcc53e1dbe5127fdd4cd026aab8864f98cdd718b2c9ad1e8fd43d892"},
     {file = "leap_ec-0.8.1.tar.gz", hash = "sha256:ae07b016068380c50b031058bdee19649e747fe846f11ed6468339f995004f99"},
@@ -1182,6 +1233,7 @@ version = "1.0.0"
 description = "File-based locks for Python on Linux and Windows"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+groups = ["main"]
 files = [
     {file = "locket-1.0.0-py2.py3-none-any.whl", hash = "sha256:b6c819a722f7b6bd955b80781788e4a66a55628b858d347536b7e81325a3a5e3"},
     {file = "locket-1.0.0.tar.gz", hash = "sha256:5c0d4c052a8bbbf750e056a8e65ccd309086f4f0f18a2eac306a8dfa4112a632"},
@@ -1193,6 +1245,7 @@ version = "1.3.2"
 description = "A super-fast templating language that borrows the best ideas from the existing templating languages."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "Mako-1.3.2-py3-none-any.whl", hash = "sha256:32a99d70754dfce237019d17ffe4a282d2d3351b9c476e90d8a60e63f133b80c"},
     {file = "Mako-1.3.2.tar.gz", hash = "sha256:2a0c8ad7f6274271b3bb7467dd37cf9cc6dab4bc19cb69a4ef10669402de698e"},
@@ -1212,6 +1265,7 @@ version = "3.0.0"
 description = "Python port of markdown-it. Markdown parsing, done right!"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "markdown-it-py-3.0.0.tar.gz", hash = "sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb"},
     {file = "markdown_it_py-3.0.0-py3-none-any.whl", hash = "sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1"},
@@ -1236,6 +1290,7 @@ version = "2.1.5"
 description = "Safely add untrusted strings to HTML/XML markup."
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
 files = [
     {file = "MarkupSafe-2.1.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a17a92de5231666cfbe003f0e4b9b3a7ae3afb1ec2845aadc2bacc93ff85febc"},
     {file = "MarkupSafe-2.1.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:72b6be590cc35924b02c78ef34b467da4ba07e4e0f0454a2c5907f473fc50ce5"},
@@ -1305,6 +1360,7 @@ version = "3.9.2"
 description = "Python plotting package"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "matplotlib-3.9.2-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:9d78bbc0cbc891ad55b4f39a48c22182e9bdaea7fc0e5dbd364f49f729ca1bbb"},
     {file = "matplotlib-3.9.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c375cc72229614632c87355366bdf2570c2dac01ac66b8ad048d2dabadf2d0d4"},
@@ -1368,6 +1424,7 @@ version = "0.1.6"
 description = "Inline Matplotlib backend for Jupyter"
 optional = false
 python-versions = ">=3.5"
+groups = ["dev"]
 files = [
     {file = "matplotlib-inline-0.1.6.tar.gz", hash = "sha256:f887e5f10ba98e8d2b150ddcf4702c1e5f8b3a20005eb0f74bfdbd360ee6f304"},
     {file = "matplotlib_inline-0.1.6-py3-none-any.whl", hash = "sha256:f1f41aab5328aa5aaea9b16d083b128102f8712542f819fe7e6a420ff581b311"},
@@ -1382,6 +1439,7 @@ version = "0.1.2"
 description = "Markdown URL utilities"
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8"},
     {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"},
@@ -1393,6 +1451,7 @@ version = "1.1.0"
 description = "MessagePack serializer"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "msgpack-1.1.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7ad442d527a7e358a469faf43fda45aaf4ac3249c8310a82f0ccff9164e5dccd"},
     {file = "msgpack-1.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:74bed8f63f8f14d75eec75cf3d04ad581da6b914001b474a5d3cd3372c8cc27d"},
@@ -1466,6 +1525,7 @@ version = "1.5.9"
 description = "Patch asyncio to allow nested event loops"
 optional = false
 python-versions = ">=3.5"
+groups = ["dev"]
 files = [
     {file = "nest_asyncio-1.5.9-py3-none-any.whl", hash = "sha256:61ec07ef052e72e3de22045b81b2cc7d71fceb04c568ba0b2e4b2f9f5231bec2"},
     {file = "nest_asyncio-1.5.9.tar.gz", hash = "sha256:d1e1144e9c6e3e6392e0fcf5211cb1c8374b5648a98f1ebe48e5336006b41907"},
@@ -1477,6 +1537,7 @@ version = "3.3"
 description = "Python package for creating and manipulating graphs and networks"
 optional = false
 python-versions = ">=3.10"
+groups = ["main"]
 files = [
     {file = "networkx-3.3-py3-none-any.whl", hash = "sha256:28575580c6ebdaf4505b22c6256a2b9de86b316dc63ba9e93abde3d78dfdbcf2"},
     {file = "networkx-3.3.tar.gz", hash = "sha256:0c127d8b2f4865f59ae9cb8aafcd60b5c70f3241ebd66f7defad7c4ab90126c9"},
@@ -1495,6 +1556,7 @@ version = "1.8.0"
 description = "Node.js virtual environment builder"
 optional = false
 python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*"
+groups = ["dev"]
 files = [
     {file = "nodeenv-1.8.0-py2.py3-none-any.whl", hash = "sha256:df865724bb3c3adc86b3876fa209771517b0cfe596beff01a92700e0e8be4cec"},
     {file = "nodeenv-1.8.0.tar.gz", hash = "sha256:d51e0c37e64fbf47d017feac3145cdbb58836d7eee8c6f6d3b6880c5456227d2"},
@@ -1509,6 +1571,7 @@ version = "1.26.3"
 description = "Fundamental package for array computing in Python"
 optional = false
 python-versions = ">=3.9"
+groups = ["main", "dev"]
 files = [
     {file = "numpy-1.26.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:806dd64230dbbfaca8a27faa64e2f414bf1c6622ab78cc4264f7f5f028fee3bf"},
     {file = "numpy-1.26.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:02f98011ba4ab17f46f80f7f8f1c291ee7d855fcef0a5a98db80767a468c85cd"},
@@ -1554,6 +1617,7 @@ version = "2.3.0"
 description = "A flexible configuration library"
 optional = false
 python-versions = ">=3.6"
+groups = ["main"]
 files = [
     {file = "omegaconf-2.3.0-py3-none-any.whl", hash = "sha256:7b4df175cdb08ba400f45cae3bdcae7ba8365db4d165fc65fd04b050ab63b46b"},
     {file = "omegaconf-2.3.0.tar.gz", hash = "sha256:d5d4b6d29955cc50ad50c46dc269bcd92c6e00f5f90d23ab5fee7bfca4ba4cc7"},
@@ -1569,6 +1633,7 @@ version = "3.5.0"
 description = "A hyperparameter optimization framework"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "optuna-3.5.0-py3-none-any.whl", hash = "sha256:4c86bbcaeff9ad5b0758e87537793f66df8f9352246315d7121ea465724a44e8"},
     {file = "optuna-3.5.0.tar.gz", hash = "sha256:ca9e1ce16aa6c6a5af0e1cc1d0cbcd98eb1c75b6a2f06be6bd9c0c5ab0698724"},
@@ -1587,9 +1652,9 @@ tqdm = "*"
 benchmark = ["asv (>=0.5.0)", "botorch", "cma", "scikit-optimize", "virtualenv"]
 checking = ["black", "blackdoc", "flake8", "isort", "mypy", "mypy-boto3-s3", "types-PyYAML", "types-redis", "types-setuptools", "types-tqdm", "typing-extensions (>=3.10.0.0)"]
 document = ["ase", "botorch", "cma", "cmaes (>=0.10.0)", "distributed", "fvcore", "lightgbm", "matplotlib (!=3.6.0)", "mlflow", "pandas", "pillow", "plotly (>=4.9.0)", "scikit-learn", "scikit-optimize", "sphinx", "sphinx-copybutton", "sphinx-gallery", "sphinx-plotly-directive", "sphinx-rtd-theme (>=1.2.0)", "torch", "torchaudio", "torchvision"]
-integration = ["botorch (>=0.4.0)", "catboost (>=0.26)", "catboost (>=0.26,<1.2)", "cma", "distributed", "lightgbm", "lightning", "mlflow", "pandas", "pytorch-ignite", "scikit-learn (>=0.24.2)", "scikit-optimize", "shap", "tensorflow", "torch", "torchaudio", "torchvision", "wandb", "xgboost"]
-optional = ["boto3", "botorch", "cmaes (>=0.10.0)", "google-cloud-storage", "matplotlib (!=3.6.0)", "pandas", "plotly (>=4.9.0)", "redis", "scikit-learn (>=0.24.2)"]
-test = ["coverage", "fakeredis[lua]", "kaleido", "moto", "pytest", "scipy (>=1.9.2)"]
+integration = ["botorch (>=0.4.0)", "catboost (>=0.26) ; sys_platform != \"darwin\"", "catboost (>=0.26,<1.2) ; sys_platform == \"darwin\"", "cma", "distributed", "lightgbm", "lightning", "mlflow", "pandas", "pytorch-ignite", "scikit-learn (>=0.24.2)", "scikit-optimize", "shap", "tensorflow", "torch", "torchaudio", "torchvision", "wandb", "xgboost"]
+optional = ["boto3", "botorch ; python_version <= \"3.11\"", "cmaes (>=0.10.0)", "google-cloud-storage", "matplotlib (!=3.6.0)", "pandas", "plotly (>=4.9.0)", "redis", "scikit-learn (>=0.24.2)"]
+test = ["coverage", "fakeredis[lua]", "kaleido", "moto", "pytest", "scipy (>=1.9.2) ; python_version >= \"3.8\""]
 
 [[package]]
 name = "optuna-dashboard"
@@ -1597,6 +1662,7 @@ version = "0.14.0"
 description = "Real-time dashboard for Optuna"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "optuna-dashboard-0.14.0.tar.gz", hash = "sha256:d3cd381a7b8b30246d3ac9014e25a3725cfaef6928807ceac96698d97badd988"},
     {file = "optuna_dashboard-0.14.0-py3-none-any.whl", hash = "sha256:e17af62420bd7888e99c6c98b696b05851de264cd74fe44e2862fe598ec3d9cb"},
@@ -1610,7 +1676,7 @@ scikit-learn = "*"
 
 [package.extras]
 docs = ["boto3", "botorch", "sphinx", "sphinx-rtd-theme", "streamlit"]
-optional = ["boto3", "botorch (>=0.8.1)", "streamlit"]
+optional = ["boto3", "botorch (>=0.8.1) ; python_version >= \"3.8\"", "streamlit"]
 preferential = ["botorch (>=0.8.1)"]
 test = ["coverage", "moto[s3]", "plotly", "pytest"]
 
@@ -1620,6 +1686,7 @@ version = "23.2"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
 files = [
     {file = "packaging-23.2-py3-none-any.whl", hash = "sha256:8c491190033a9af7e1d931d0b5dacc2ef47509b34dd0de67ed209b5203fc88c7"},
     {file = "packaging-23.2.tar.gz", hash = "sha256:048fb0e9405036518eaaf48a55953c750c11e1a1b68e0dd1a9d62ed0c092cfc5"},
@@ -1631,6 +1698,7 @@ version = "2.2.2"
 description = "Powerful data structures for data analysis, time series, and statistics"
 optional = false
 python-versions = ">=3.9"
+groups = ["main", "dev"]
 files = [
     {file = "pandas-2.2.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:90c6fca2acf139569e74e8781709dccb6fe25940488755716d1d354d6bc58bce"},
     {file = "pandas-2.2.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c7adfc142dac335d8c1e0dcbd37eb8617eac386596eb9e1a1b77791cf2498238"},
@@ -1704,6 +1772,7 @@ version = "0.8.3"
 description = "A Python Parser"
 optional = false
 python-versions = ">=3.6"
+groups = ["dev"]
 files = [
     {file = "parso-0.8.3-py2.py3-none-any.whl", hash = "sha256:c001d4636cd3aecdaf33cbb40aebb59b094be2a74c556778ef5576c175e19e75"},
     {file = "parso-0.8.3.tar.gz", hash = "sha256:8c07be290bb59f03588915921e29e8a50002acaf2cdc5fa0e0114f91709fafa0"},
@@ -1719,6 +1788,7 @@ version = "1.4.2"
 description = "Appendable key-value storage"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "partd-1.4.2-py3-none-any.whl", hash = "sha256:978e4ac767ec4ba5b86c6eaa52e5a2a3bc748a2ca839e8cc798f1cc6ce6efb0f"},
     {file = "partd-1.4.2.tar.gz", hash = "sha256:d022c33afbdc8405c226621b015e8067888173d85f7f5ecebb3cafed9a20f02c"},
@@ -1737,6 +1807,7 @@ version = "0.5.6"
 description = "A Python package for describing statistical models and for building design matrices."
 optional = false
 python-versions = "*"
+groups = ["main"]
 files = [
     {file = "patsy-0.5.6-py2.py3-none-any.whl", hash = "sha256:19056886fd8fa71863fa32f0eb090267f21fb74be00f19f5c70b2e9d76c883c6"},
     {file = "patsy-0.5.6.tar.gz", hash = "sha256:95c6d47a7222535f84bff7f63d7303f2e297747a598db89cf5c67f0c0c7d2cdb"},
@@ -1755,6 +1826,8 @@ version = "4.9.0"
 description = "Pexpect allows easy control of interactive console applications."
 optional = false
 python-versions = "*"
+groups = ["dev"]
+markers = "sys_platform != \"win32\""
 files = [
     {file = "pexpect-4.9.0-py2.py3-none-any.whl", hash = "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523"},
     {file = "pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f"},
@@ -1769,6 +1842,7 @@ version = "10.2.0"
 description = "Python Imaging Library (Fork)"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "pillow-10.2.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:7823bdd049099efa16e4246bdf15e5a13dbb18a51b68fa06d6c1d4d8b99a796e"},
     {file = "pillow-10.2.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:83b2021f2ade7d1ed556bc50a399127d7fb245e725aa0113ebd05cfe88aaf588"},
@@ -1845,7 +1919,7 @@ docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline
 fpx = ["olefile"]
 mic = ["olefile"]
 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
-typing = ["typing-extensions"]
+typing = ["typing-extensions ; python_version < \"3.10\""]
 xmp = ["defusedxml"]
 
 [[package]]
@@ -1854,6 +1928,7 @@ version = "4.1.0"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "platformdirs-4.1.0-py3-none-any.whl", hash = "sha256:11c8f37bcca40db96d8144522d925583bdb7a31f7b0e37e3ed4318400a8e2380"},
     {file = "platformdirs-4.1.0.tar.gz", hash = "sha256:906d548203468492d432bcb294d4bc2fff751bf84971fbb2c10918cc206ee420"},
@@ -1869,6 +1944,7 @@ version = "5.24.0"
 description = "An open-source, interactive data visualization library for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "plotly-5.24.0-py3-none-any.whl", hash = "sha256:0e54efe52c8cef899f7daa41be9ed97dfb6be622613a2a8f56a86a0634b2b67e"},
     {file = "plotly-5.24.0.tar.gz", hash = "sha256:eae9f4f54448682442c92c1e97148e3ad0c52f0cf86306e1b76daba24add554a"},
@@ -1884,6 +1960,7 @@ version = "1.4.0"
 description = "plugin and hook calling mechanisms for python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "pluggy-1.4.0-py3-none-any.whl", hash = "sha256:7db9f7b503d67d1c5b95f59773ebb58a8c1c288129a88665838012cfb07b8981"},
     {file = "pluggy-1.4.0.tar.gz", hash = "sha256:8c85c2876142a764e5b7548e7d9a0e0ddb46f5185161049a79b7e974454223be"},
@@ -1899,6 +1976,7 @@ version = "3.6.2"
 description = "A framework for managing and maintaining multi-language pre-commit hooks."
 optional = false
 python-versions = ">=3.9"
+groups = ["dev"]
 files = [
     {file = "pre_commit-3.6.2-py2.py3-none-any.whl", hash = "sha256:ba637c2d7a670c10daedc059f5c49b5bd0aadbccfcd7ec15592cf9665117532c"},
     {file = "pre_commit-3.6.2.tar.gz", hash = "sha256:c3ef34f463045c88658c5b99f38c1e297abdcc0ff13f98d3370055fbbfabc67e"},
@@ -1917,6 +1995,7 @@ version = "3.0.43"
 description = "Library for building powerful interactive command lines in Python"
 optional = false
 python-versions = ">=3.7.0"
+groups = ["dev"]
 files = [
     {file = "prompt_toolkit-3.0.43-py3-none-any.whl", hash = "sha256:a11a29cb3bf0a28a387fe5122cdb649816a957cd9261dcedf8c9f1fef33eacf6"},
     {file = "prompt_toolkit-3.0.43.tar.gz", hash = "sha256:3527b7af26106cbc65a040bcc84839a3566ec1b051bb0bfe953631e704b0ff7d"},
@@ -1931,6 +2010,7 @@ version = "5.9.7"
 description = "Cross-platform lib for process and system monitoring in Python."
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
+groups = ["main", "dev"]
 files = [
     {file = "psutil-5.9.7-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:0bd41bf2d1463dfa535942b2a8f0e958acf6607ac0be52265ab31f7923bcd5e6"},
     {file = "psutil-5.9.7-cp27-cp27m-manylinux2010_i686.whl", hash = "sha256:5794944462509e49d4d458f4dbfb92c47539e7d8d15c796f141f474010084056"},
@@ -1951,7 +2031,7 @@ files = [
 ]
 
 [package.extras]
-test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
+test = ["enum34 ; python_version <= \"3.4\"", "ipaddress ; python_version < \"3.0\"", "mock ; python_version < \"3.0\"", "pywin32 ; sys_platform == \"win32\"", "wmi ; sys_platform == \"win32\""]
 
 [[package]]
 name = "ptyprocess"
@@ -1959,6 +2039,8 @@ version = "0.7.0"
 description = "Run a subprocess in a pseudo terminal"
 optional = false
 python-versions = "*"
+groups = ["dev"]
+markers = "sys_platform != \"win32\""
 files = [
     {file = "ptyprocess-0.7.0-py2.py3-none-any.whl", hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35"},
     {file = "ptyprocess-0.7.0.tar.gz", hash = "sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220"},
@@ -1970,6 +2052,7 @@ version = "0.2.2"
 description = "Safely evaluate AST nodes without side effects"
 optional = false
 python-versions = "*"
+groups = ["dev"]
 files = [
     {file = "pure_eval-0.2.2-py3-none-any.whl", hash = "sha256:01eaab343580944bc56080ebe0a674b39ec44a945e6d09ba7db3cb8cec289350"},
     {file = "pure_eval-0.2.2.tar.gz", hash = "sha256:2b45320af6dfaa1750f543d714b6d1c520a1688dec6fd24d339063ce0aaa9ac3"},
@@ -1984,6 +2067,7 @@ version = "1.1"
 description = "A Python Advanced Differential Evolution algorithms library"
 optional = false
 python-versions = "*"
+groups = ["main"]
 files = [
     {file = "pyade-python-1.1.tar.gz", hash = "sha256:dc90529274ef1b1eb163d14f6c360ef7902f3c771dde3007ab77e128fd13fa53"},
     {file = "pyade_python-1.1-py3-none-any.whl", hash = "sha256:c3fed6001a8f927a7c20a5ea7e87fea3fd69323e1dabbc18b94bf41adb32e719"},
@@ -1995,6 +2079,8 @@ version = "2.21"
 description = "C parser in Python"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+groups = ["dev"]
+markers = "implementation_name == \"pypy\""
 files = [
     {file = "pycparser-2.21-py2.py3-none-any.whl", hash = "sha256:8ee45429555515e1f6b185e78100aea234072576aa43ab53aefcae078162fca9"},
     {file = "pycparser-2.21.tar.gz", hash = "sha256:e644fdec12f7872f86c58ff790da456218b10f863970249516d60a5eaca77206"},
@@ -2006,6 +2092,7 @@ version = "2.0.1.4"
 description = "Python bindings for ERFA"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "pyerfa-2.0.1.4-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:ff112353944bf705342741f2fe41674f97154a302b0295eaef7381af92ad2b3a"},
     {file = "pyerfa-2.0.1.4-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:900b266a3862baa9560d6b1b184dcc14e0e76d550ff70d32336d3989b2ed18ca"},
@@ -2033,13 +2120,14 @@ version = "2.17.2"
 description = "Pygments is a syntax highlighting package written in Python."
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
 files = [
     {file = "pygments-2.17.2-py3-none-any.whl", hash = "sha256:b27c2826c47d0f3219f29554824c30c5e8945175d888647acd804ddd04af846c"},
     {file = "pygments-2.17.2.tar.gz", hash = "sha256:da46cec9fd2de5be3a8a784f434e4c4ab670b4ff54d605c4c2717e9d49c4c367"},
 ]
 
 [package.extras]
-plugins = ["importlib-metadata"]
+plugins = ["importlib-metadata ; python_version < \"3.8\""]
 windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
@@ -2048,6 +2136,7 @@ version = "0.1.1"
 description = "The HMS (Hierarchic Memetic Strategy) is a composite global optimization strategy consisting of a multi-population evolutionary strategy and some auxiliary methods. The HMS makes use of a tree with a fixed maximal height and variable internal node degree. Each component population is governed by a particular evolutionary engine. This package provides a simple python implementation with examples of using different population engines."
 optional = false
 python-versions = "^3.10"
+groups = ["main"]
 files = []
 develop = false
 
@@ -2078,6 +2167,7 @@ version = "1.7.2"
 description = "Kriging Toolkit for Python."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "PyKrige-1.7.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5975b5fd939266835c197289dbdb2dd2c2408f9cb56246019c43d5353eaa3ea3"},
     {file = "PyKrige-1.7.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:54bf6252e3fa91a6d798e7d9669108154d2f6866283da6f6029d2813667e64ea"},
@@ -2119,6 +2209,7 @@ version = "3.1.1"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
 optional = false
 python-versions = ">=3.6.8"
+groups = ["main"]
 files = [
     {file = "pyparsing-3.1.1-py3-none-any.whl", hash = "sha256:32c7c0b711493c72ff18a981d24f28aaf9c1fb7ed5e9667c9e84e3db623bdbfb"},
     {file = "pyparsing-3.1.1.tar.gz", hash = "sha256:ede28a1a32462f5a9705e07aea48001a08f7cf81a021585011deba701581a0db"},
@@ -2133,6 +2224,7 @@ version = "8.0.1"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "pytest-8.0.1-py3-none-any.whl", hash = "sha256:3e4f16fe1c0a9dc9d9389161c127c3edc5d810c38d6793042fb81d9f48a59fca"},
     {file = "pytest-8.0.1.tar.gz", hash = "sha256:267f6563751877d772019b13aacbe4e860d73fe8f651f28112e9ac37de7513ae"},
@@ -2155,6 +2247,7 @@ version = "4.1.0"
 description = "Pytest plugin for measuring coverage."
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "pytest-cov-4.1.0.tar.gz", hash = "sha256:3904b13dfbfec47f003b8e77fd5b589cd11904a21ddf1ab38a64f204d6a10ef6"},
     {file = "pytest_cov-4.1.0-py3-none-any.whl", hash = "sha256:6ba70b9e97e69fcc3fb45bfeab2d0a138fb65c4d0d6a41ef33983ad114be8c3a"},
@@ -2173,6 +2266,7 @@ version = "2.8.2"
 description = "Extensions to the standard Python datetime module"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+groups = ["main", "dev"]
 files = [
     {file = "python-dateutil-2.8.2.tar.gz", hash = "sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86"},
     {file = "python_dateutil-2.8.2-py2.py3-none-any.whl", hash = "sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9"},
@@ -2187,6 +2281,7 @@ version = "2023.3.post1"
 description = "World timezone definitions, modern and historical"
 optional = false
 python-versions = "*"
+groups = ["main", "dev"]
 files = [
     {file = "pytz-2023.3.post1-py2.py3-none-any.whl", hash = "sha256:ce42d816b81b68506614c11e8937d3aa9e41007ceb50bfdcb0749b921bf646c7"},
     {file = "pytz-2023.3.post1.tar.gz", hash = "sha256:7b4fddbeb94a1eba4b557da24f19fdf9db575192544270a9101d8509f9f43d7b"},
@@ -2198,6 +2293,8 @@ version = "306"
 description = "Python for Window Extensions"
 optional = false
 python-versions = "*"
+groups = ["dev"]
+markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""
 files = [
     {file = "pywin32-306-cp310-cp310-win32.whl", hash = "sha256:06d3420a5155ba65f0b72f2699b5bacf3109f36acbe8923765c22938a69dfc8d"},
     {file = "pywin32-306-cp310-cp310-win_amd64.whl", hash = "sha256:84f4471dbca1887ea3803d8848a1616429ac94a4a8d05f4bc9c5dcfd42ca99c8"},
@@ -2221,6 +2318,7 @@ version = "6.0.1"
 description = "YAML parser and emitter for Python"
 optional = false
 python-versions = ">=3.6"
+groups = ["main", "dev"]
 files = [
     {file = "PyYAML-6.0.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d858aa552c999bc8a8d57426ed01e40bef403cd8ccdd0fc5f6f04a00414cac2a"},
     {file = "PyYAML-6.0.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:fd66fc5d0da6d9815ba2cebeb4205f95818ff4b79c3ebe268e75d961704af52f"},
@@ -2270,6 +2368,7 @@ version = "25.1.2"
 description = "Python bindings for 0MQ"
 optional = false
 python-versions = ">=3.6"
+groups = ["dev"]
 files = [
     {file = "pyzmq-25.1.2-cp310-cp310-macosx_10_15_universal2.whl", hash = "sha256:e624c789359f1a16f83f35e2c705d07663ff2b4d4479bad35621178d8f0f6ea4"},
     {file = "pyzmq-25.1.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:49151b0efece79f6a79d41a461d78535356136ee70084a1c22532fc6383f4ad0"},
@@ -2375,6 +2474,7 @@ version = "13.8.1"
 description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
 optional = false
 python-versions = ">=3.7.0"
+groups = ["main"]
 files = [
     {file = "rich-13.8.1-py3-none-any.whl", hash = "sha256:1760a3c0848469b97b558fc61c85233e3dafb69c7a071b4d60c38099d3cd4c06"},
     {file = "rich-13.8.1.tar.gz", hash = "sha256:8260cda28e3db6bf04d2d1ef4dbc03ba80a824c88b0e7668a0f23126a424844a"},
@@ -2393,6 +2493,7 @@ version = "1.5.2"
 description = "A set of python modules for machine learning and data mining"
 optional = false
 python-versions = ">=3.9"
+groups = ["main", "dev"]
 files = [
     {file = "scikit_learn-1.5.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:299406827fb9a4f862626d0fe6c122f5f87f8910b86fe5daa4c32dcd742139b6"},
     {file = "scikit_learn-1.5.2-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:2d4cad1119c77930b235579ad0dc25e65c917e756fe80cab96aa3b9428bd3fb0"},
@@ -2432,12 +2533,36 @@ install = ["joblib (>=1.2.0)", "numpy (>=1.19.5)", "scipy (>=1.6.0)", "threadpoo
 maintenance = ["conda-lock (==2.5.6)"]
 tests = ["black (>=24.3.0)", "matplotlib (>=3.3.4)", "mypy (>=1.9)", "numpydoc (>=1.2.0)", "pandas (>=1.1.5)", "polars (>=0.20.30)", "pooch (>=1.6.0)", "pyamg (>=4.0.0)", "pyarrow (>=12.0.0)", "pytest (>=7.1.2)", "pytest-cov (>=2.9.0)", "ruff (>=0.2.1)", "scikit-image (>=0.17.2)"]
 
+[[package]]
+name = "scikit-posthocs"
+version = "0.11.2"
+description = "Statistical post-hoc analysis and outlier detection algorithms"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "scikit_posthocs-0.11.2-py3-none-any.whl", hash = "sha256:adaa21547c2e3c34eec9d86d6a06e379552a187c87bc06e2b71e9f00cf57b86a"},
+    {file = "scikit_posthocs-0.11.2.tar.gz", hash = "sha256:805a30350497341ee3a573bb92953515dbb4c4c7b4bac5def09721a98a3489d2"},
+]
+
+[package.dependencies]
+matplotlib = "*"
+numpy = "*"
+pandas = ">=0.20.0"
+scipy = ">=1.9.0"
+seaborn = "*"
+statsmodels = "*"
+
+[package.extras]
+test = ["coverage", "pytest"]
+
 [[package]]
 name = "scipy"
 version = "1.14.1"
 description = "Fundamental algorithms for scientific computing in Python"
 optional = false
 python-versions = ">=3.10"
+groups = ["main", "dev"]
 files = [
     {file = "scipy-1.14.1-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:b28d2ca4add7ac16ae8bb6632a3c86e4b9e4d52d3e34267f6e1b0c1f8d87e389"},
     {file = "scipy-1.14.1-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:d0d2821003174de06b69e58cef2316a6622b60ee613121199cb2852a873f8cf3"},
@@ -2480,7 +2605,29 @@ numpy = ">=1.23.5,<2.3"
 [package.extras]
 dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodestyle", "pydevtool", "rich-click", "ruff (>=0.0.292)", "types-psutil", "typing_extensions"]
 doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.13.1)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<=7.3.7)", "sphinx-design (>=0.4.0)"]
-test = ["Cython", "array-api-strict (>=2.0)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+test = ["Cython", "array-api-strict (>=2.0)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja ; sys_platform != \"emscripten\"", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+
+[[package]]
+name = "seaborn"
+version = "0.13.2"
+description = "Statistical data visualization"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "seaborn-0.13.2-py3-none-any.whl", hash = "sha256:636f8336facf092165e27924f223d3c62ca560b1f2bb5dff7ab7fad265361987"},
+    {file = "seaborn-0.13.2.tar.gz", hash = "sha256:93e60a40988f4d65e9f4885df477e2fdaff6b73a9ded434c1ab356dd57eefff7"},
+]
+
+[package.dependencies]
+matplotlib = ">=3.4,<3.6.1 || >3.6.1"
+numpy = ">=1.20,<1.24.0 || >1.24.0"
+pandas = ">=1.2"
+
+[package.extras]
+dev = ["flake8", "flit", "mypy", "pandas-stubs", "pre-commit", "pytest", "pytest-cov", "pytest-xdist"]
+docs = ["ipykernel", "nbconvert", "numpydoc", "pydata_sphinx_theme (==0.10.0rc2)", "pyyaml", "sphinx (<6.0.0)", "sphinx-copybutton", "sphinx-design", "sphinx-issues"]
+stats = ["scipy (>=1.7)", "statsmodels (>=0.12)"]
 
 [[package]]
 name = "setuptools"
@@ -2488,6 +2635,7 @@ version = "69.1.1"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "setuptools-69.1.1-py3-none-any.whl", hash = "sha256:02fa291a0471b3a18b2b2481ed902af520c69e8ae0919c13da936542754b4c56"},
     {file = "setuptools-69.1.1.tar.gz", hash = "sha256:5c0806c7d9af348e6dd3777b4f4dbb42c7ad85b190104837488eab9a7c945cf8"},
@@ -2495,7 +2643,7 @@ files = [
 
 [package.extras]
 docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
-testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
+testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21) ; python_version >= \"3.9\" and sys_platform != \"cygwin\"", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov ; platform_python_implementation != \"PyPy\"", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy (>=0.9.1) ; platform_python_implementation != \"PyPy\"", "pytest-perf ; sys_platform != \"cygwin\"", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\"", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
 testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.2)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
 
 [[package]]
@@ -2504,6 +2652,7 @@ version = "1.16.0"
 description = "Python 2 and 3 compatibility utilities"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
+groups = ["main", "dev"]
 files = [
     {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
     {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
@@ -2515,6 +2664,7 @@ version = "2.4.0"
 description = "Sorted Containers -- Sorted List, Sorted Dict, Sorted Set"
 optional = false
 python-versions = "*"
+groups = ["main"]
 files = [
     {file = "sortedcontainers-2.4.0-py2.py3-none-any.whl", hash = "sha256:a163dcaede0f1c021485e957a39245190e74249897e2ae4b2aa38595db237ee0"},
     {file = "sortedcontainers-2.4.0.tar.gz", hash = "sha256:25caa5a06cc30b6b83d11423433f65d1f9d76c4c6a0c90e3379eaa43b9bfdb88"},
@@ -2526,6 +2676,7 @@ version = "2.0.25"
 description = "Database Abstraction Library"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "SQLAlchemy-2.0.25-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:4344d059265cc8b1b1be351bfb88749294b87a8b2bbe21dfbe066c4199541ebd"},
     {file = "SQLAlchemy-2.0.25-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6f9e2e59cbcc6ba1488404aad43de005d05ca56e069477b33ff74e91b6319735"},
@@ -2613,6 +2764,7 @@ version = "0.6.3"
 description = "Extract data from python stack frames and tracebacks for informative displays"
 optional = false
 python-versions = "*"
+groups = ["dev"]
 files = [
     {file = "stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695"},
     {file = "stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9"},
@@ -2632,6 +2784,7 @@ version = "0.14.2"
 description = "Statistical computations and models for Python"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "statsmodels-0.14.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:df5d6f95c46f0341da6c79ee7617e025bf2b371e190a8e60af1ae9cabbdb7a97"},
     {file = "statsmodels-0.14.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:a87ef21fadb445b650f327340dde703f13aec1540f3d497afb66324499dea97a"},
@@ -2668,7 +2821,7 @@ scipy = ">=1.8,<1.9.2 || >1.9.2"
 
 [package.extras]
 build = ["cython (>=0.29.33)"]
-develop = ["colorama", "cython (>=0.29.33)", "cython (>=3.0.10,<4)", "flake8", "isort", "joblib", "matplotlib (>=3)", "pytest (>=7.3.0,<8)", "pytest-cov", "pytest-randomly", "pytest-xdist", "pywinpty", "setuptools-scm[toml] (>=8.0,<9.0)"]
+develop = ["colorama", "cython (>=0.29.33)", "cython (>=3.0.10,<4)", "flake8", "isort", "joblib", "matplotlib (>=3)", "pytest (>=7.3.0,<8)", "pytest-cov", "pytest-randomly", "pytest-xdist", "pywinpty ; os_name == \"nt\"", "setuptools-scm[toml] (>=8.0,<9.0)"]
 docs = ["ipykernel", "jupyter-client", "matplotlib", "nbconvert", "nbformat", "numpydoc", "pandas-datareader", "sphinx"]
 
 [[package]]
@@ -2677,6 +2830,7 @@ version = "24.1.0"
 description = "Structured Logging for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "structlog-24.1.0-py3-none-any.whl", hash = "sha256:3f6efe7d25fab6e86f277713c218044669906537bb717c1807a09d46bca0714d"},
     {file = "structlog-24.1.0.tar.gz", hash = "sha256:41a09886e4d55df25bdcb9b5c9674bccfab723ff43e0a86a1b7b236be8e57b16"},
@@ -2688,12 +2842,28 @@ docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-
 tests = ["freezegun (>=0.2.8)", "pretend", "pytest (>=6.0)", "pytest-asyncio (>=0.17)", "simplejson"]
 typing = ["mypy (>=1.4)", "rich", "twisted"]
 
+[[package]]
+name = "tabulate"
+version = "0.9.0"
+description = "Pretty-print tabular data"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "tabulate-0.9.0-py3-none-any.whl", hash = "sha256:024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f"},
+    {file = "tabulate-0.9.0.tar.gz", hash = "sha256:0095b12bf5966de529c0feb1fa08671671b3368eec77d7ef7ab114be2c068b3c"},
+]
+
+[package.extras]
+widechars = ["wcwidth"]
+
 [[package]]
 name = "tblib"
 version = "3.0.0"
 description = "Traceback serialization library."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "tblib-3.0.0-py3-none-any.whl", hash = "sha256:80a6c77e59b55e83911e1e607c649836a69c103963c5f28a46cbeef44acf8129"},
     {file = "tblib-3.0.0.tar.gz", hash = "sha256:93622790a0a29e04f0346458face1e144dc4d32f493714c6c3dff82a4adb77e6"},
@@ -2705,6 +2875,7 @@ version = "9.0.0"
 description = "Retry code until it succeeds"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "tenacity-9.0.0-py3-none-any.whl", hash = "sha256:93de0c98785b27fcf659856aa9f54bfbd399e29969b0621bc7f762bd441b4539"},
     {file = "tenacity-9.0.0.tar.gz", hash = "sha256:807f37ca97d62aa361264d497b0e31e92b8027044942bfa756160d908320d73b"},
@@ -2720,6 +2891,7 @@ version = "3.2.0"
 description = "threadpoolctl"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
 files = [
     {file = "threadpoolctl-3.2.0-py3-none-any.whl", hash = "sha256:2b7818516e423bdaebb97c723f86a7c6b0a83d3f3b0970328d66f4d9104dc032"},
     {file = "threadpoolctl-3.2.0.tar.gz", hash = "sha256:c96a0ba3bdddeaca37dc4cc7344aafad41cdb8c313f74fdfe387a867bba93355"},
@@ -2731,6 +2903,8 @@ version = "2.0.1"
 description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version < \"3.11\""
 files = [
     {file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"},
     {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
@@ -2742,6 +2916,7 @@ version = "0.12.1"
 description = "List processing tools and functional utilities"
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "toolz-0.12.1-py3-none-any.whl", hash = "sha256:d22731364c07d72eea0a0ad45bafb2c2937ab6fd38a3507bf55eae8744aa7d85"},
     {file = "toolz-0.12.1.tar.gz", hash = "sha256:ecca342664893f177a13dac0e6b41cbd8ac25a358e5f215316d43e2100224f4d"},
@@ -2753,6 +2928,7 @@ version = "6.4"
 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
 optional = false
 python-versions = ">= 3.8"
+groups = ["main", "dev"]
 files = [
     {file = "tornado-6.4-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:02ccefc7d8211e5a7f9e8bc3f9e5b0ad6262ba2fbb683a6443ecc804e5224ce0"},
     {file = "tornado-6.4-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:27787de946a9cffd63ce5814c33f734c627a87072ec7eed71f7fc4417bb16263"},
@@ -2773,6 +2949,7 @@ version = "4.66.1"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "tqdm-4.66.1-py3-none-any.whl", hash = "sha256:d302b3c5b53d47bce91fea46679d9c3c6508cf6332229aa1e7d8653723793386"},
     {file = "tqdm-4.66.1.tar.gz", hash = "sha256:d88e651f9db8d8551a62556d3cff9e3034274ca5d66e93197cf2490e2dcb69c7"},
@@ -2793,6 +2970,7 @@ version = "5.14.1"
 description = "Traitlets Python configuration system"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "traitlets-5.14.1-py3-none-any.whl", hash = "sha256:2e5a030e6eff91737c643231bfcf04a65b0132078dad75e4936700b213652e74"},
     {file = "traitlets-5.14.1.tar.gz", hash = "sha256:8585105b371a04b8316a43d5ce29c098575c2e477850b62b848b964f1444527e"},
@@ -2808,6 +2986,7 @@ version = "1.7.0"
 description = "A Python implementation of tree structure."
 optional = false
 python-versions = "*"
+groups = ["main"]
 files = [
     {file = "treelib-1.7.0-py3-none-any.whl", hash = "sha256:c37795eaba19f73f3e1a905ef3f4f0cab660dc7617126e8ae99391e25c755416"},
     {file = "treelib-1.7.0.tar.gz", hash = "sha256:9bff1af416b9e642a6cd0e0431d15edf26a24b8d0c8ae68afbd3801b5e30fb61"},
@@ -2822,6 +3001,7 @@ version = "4.9.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
 files = [
     {file = "typing_extensions-4.9.0-py3-none-any.whl", hash = "sha256:af72aea155e91adfc61c3ae9e0e342dbc0cba726d6cba4b6c72c1f34e47291cd"},
     {file = "typing_extensions-4.9.0.tar.gz", hash = "sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783"},
@@ -2833,6 +3013,7 @@ version = "2023.4"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
+groups = ["main", "dev"]
 files = [
     {file = "tzdata-2023.4-py2.py3-none-any.whl", hash = "sha256:aa3ace4329eeacda5b7beb7ea08ece826c28d761cda36e747cfbf97996d39bf3"},
     {file = "tzdata-2023.4.tar.gz", hash = "sha256:dd54c94f294765522c77399649b4fefd95522479a664a0cec87f41bebc6148c9"},
@@ -2844,13 +3025,14 @@ version = "2.2.2"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "urllib3-2.2.2-py3-none-any.whl", hash = "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472"},
     {file = "urllib3-2.2.2.tar.gz", hash = "sha256:dd505485549a7a552833da5e6063639d0d177c04f23bc3864e41e5dc5f612168"},
 ]
 
 [package.extras]
-brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+brotli = ["brotli (>=1.0.9) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\""]
 h2 = ["h2 (>=4,<5)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
@@ -2861,6 +3043,7 @@ version = "20.25.1"
 description = "Virtual Python Environment builder"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
 files = [
     {file = "virtualenv-20.25.1-py3-none-any.whl", hash = "sha256:961c026ac520bac5f69acb8ea063e8a4f071bcc9457b9c1f28f6b085c511583a"},
     {file = "virtualenv-20.25.1.tar.gz", hash = "sha256:e08e13ecdca7a0bd53798f356d5831434afa5b07b93f0abdf0797b7a06ffe197"},
@@ -2873,7 +3056,7 @@ platformdirs = ">=3.9.1,<5"
 
 [package.extras]
 docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"]
-test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"]
+test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8) ; platform_python_implementation == \"PyPy\"", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10) ; platform_python_implementation == \"CPython\""]
 
 [[package]]
 name = "wcwidth"
@@ -2881,6 +3064,7 @@ version = "0.2.13"
 description = "Measures the displayed width of unicode strings in a terminal"
 optional = false
 python-versions = "*"
+groups = ["dev"]
 files = [
     {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"},
     {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"},
@@ -2892,6 +3076,7 @@ version = "3.0.0"
 description = "Mutable mapping tools"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "zict-3.0.0-py2.py3-none-any.whl", hash = "sha256:5796e36bd0e0cc8cf0fbc1ace6a68912611c1dbd74750a3f3026b9b9d6a327ae"},
     {file = "zict-3.0.0.tar.gz", hash = "sha256:e321e263b6a97aafc0790c3cfb3c04656b7066e6738c37fffcca95d803c9fba5"},
@@ -2903,20 +3088,22 @@ version = "3.20.1"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version <= \"3.11\""
 files = [
     {file = "zipp-3.20.1-py3-none-any.whl", hash = "sha256:9960cd8967c8f85a56f920d5d507274e74f9ff813a0ab8889a5b5be2daf44064"},
     {file = "zipp-3.20.1.tar.gz", hash = "sha256:c22b14cc4763c5a5b04134207736c107db42e9d3ef2d9779d465f5f1bcba572b"},
 ]
 
 [package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
 cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 enabler = ["pytest-enabler (>=2.2)"]
-test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
+test = ["big-O", "importlib-resources ; python_version < \"3.9\"", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
 type = ["pytest-mypy"]
 
 [metadata]
-lock-version = "2.0"
+lock-version = "2.1"
 python-versions = "^3.10"
-content-hash = "8f4d93b5360c873aa7b4647d744845ace1b75dfe8b43ad841c736cb87a65bb3b"
+content-hash = "5996c160b61a221ba7532b49f26fdb409d3134ba9597d7fe8374cdfcd1afa6ab"
diff --git a/pyproject.toml b/pyproject.toml
index 52ace6c..eb4b731 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -30,6 +30,8 @@ landscape-analysis-toolbox = {git = "https://github.com/agh-a2s/landscape-analys
 leap-ec = "^0.8.1"
 statsmodels = "^0.14.2"
 astropy = "^6.1.4"
+tabulate = "^0.9.0"
+scikit-posthocs = "^0.11.2"
 
 
 [tool.poetry.group.dev.dependencies]
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+1518,iLSHADE Mann-Wald,[0.82395504 0.19623915 0.0749866  0.07601369 0.19420023 0.57714667],1.3519198329886422,0.04288636133234508,[0.80318441 0.19681559 0.074866   0.08248581 0.19279503 0.58189488],0.04657231762556006,2,37,10,0.0014561253021236897,0.0003921568777597024
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+1981,GA Mann-Wald,[0.71965699 0.28034301 0.02977878 0.02073357 0.06096586 0.9886663 ],1.67772875004448,0.034501908669643366,[0.71881416 0.28118584 0.02919336 0.02074347 0.06038217 0.98944288],0.0338380060805389,2,49,6,0.0005742153711671971,0.00023858337649208073
+1982,iLSHADE Mann-Wald,[0.73190207 0.28300169 0.02992489 0.02033619 0.05981612 0.98928748],1.436266417033039,0.03232284006814328,[0.71881416 0.28118584 0.02919336 0.02074347 0.06038217 0.98944288],0.0338380060805389,2,49,6,0.0007260210028805503,0.00016421662524237972
+1983,EM,[0.71885943 0.28114057 0.02953711 0.02057002 0.05960595 0.98869891],0.061587125062942505,0.03330812380137573,[0.71881416 0.28118584 0.02919336 0.02074347 0.06038217 0.98944288],0.0338380060805389,2,49,6,0.000519999718781694,0.0001336290939847467
+1984,DO,[0.71875032 0.28124968 0.00122009 0.01675895 0.05913049 0.99047304],1.5437113750958815,0.0,[0.71881416 0.28118584 0.02919336 0.02074347 0.06038217 0.98944288],0.0006426131383956054,2,49,7,195.78302373331223,0.39452072398538396
+1985,GA Mann-Wald,[0.71904404 0.28095596 0.03043453 0.02027381 0.06112449 0.98935111],1.7826144580030814,0.03486671679449913,[0.71881416 0.28118584 0.02919336 0.02074347 0.06038217 0.98944288],0.0338380060805389,2,49,7,0.001561846779918774,0.0005558466734485593
+1986,iLSHADE Mann-Wald,[0.73179097 0.28628536 0.02993047 0.02015331 0.05982578 0.98940405],1.4226206250023097,0.03235067904278439,[0.71881416 0.28118584 0.02919336 0.02074347 0.06038217 0.98944288],0.0338380060805389,2,49,7,0.0008330233173743373,0.00022353076093259596
+1987,EM,[0.71885943 0.28114057 0.02953711 0.02057002 0.05960595 0.98869891],0.43273808306548744,0.03330812380137573,[0.71881416 0.28118584 0.02919336 0.02074347 0.06038217 0.98944288],0.0338380060805389,2,49,7,0.0005303041525897001,0.000129644500438133
+1988,DO,[0.71993366 0.28006634 0.03235727 0.01295135 0.05938406 0.98361586],1.535228291992098,0.0,[0.71881416 0.28118584 0.02919336 0.02074347 0.06038217 0.98944288],0.0006426131383956054,2,49,8,0.12466953644293532,0.019374160531851335
+1989,GA Mann-Wald,[0.7203565  0.2796435  0.03012969 0.0204674  0.06142104 0.98765641],1.6732866669772193,0.03708721201249357,[0.71881416 0.28118584 0.02919336 0.02074347 0.06038217 0.98944288],0.0338380060805389,2,49,8,0.0025752442887614357,0.0006694527045567677
+1990,iLSHADE Mann-Wald,[0.73179257 0.28628494 0.02993043 0.02015333 0.05982571 0.98940403],1.307294209022075,0.0323506790331576,[0.71881416 0.28118584 0.02919336 0.02074347 0.06038217 0.98944288],0.0338380060805389,2,49,8,0.0008738281252056446,0.00022438124957151662
+1991,EM,[0.71885943 0.28114057 0.02953711 0.02057002 0.05960595 0.98869891],0.05974008399061859,0.03330812380137573,[0.71881416 0.28118584 0.02919336 0.02074347 0.06038217 0.98944288],0.0338380060805389,2,49,8,0.00044239440369042704,6.641379374801949e-05
+1992,DO,[0.71937398 0.28062602 0.0213838  0.01894138 0.05922591 0.98346221],1.7344222079264,0.0,[0.71881416 0.28118584 0.02919336 0.02074347 0.06038217 0.98944288],0.0006426131383956054,2,49,9,0.10590127314938269,0.020331100957339303
+1993,GA Mann-Wald,[0.71165722 0.28834278 0.0299205  0.02067276 0.05974738 0.98888193],1.7685085000703111,0.03318209260726539,[0.71881416 0.28118584 0.02919336 0.02074347 0.06038217 0.98944288],0.0338380060805389,2,49,9,0.0007463819173809672,0.00017157956255410626
+1994,iLSHADE Mann-Wald,[0.73150431 0.28465207 0.02990767 0.02040929 0.05978102 0.98916815],1.5136845000088215,0.032338282789215515,[0.71881416 0.28118584 0.02919336 0.02074347 0.06038217 0.98944288],0.0338380060805389,2,49,9,0.000611913835607325,0.00019804885029056591
+1995,EM,[0.71885943 0.28114057 0.02953711 0.02057002 0.05960595 0.98869891],0.16811012499965727,0.03330812380137573,[0.71881416 0.28118584 0.02919336 0.02074347 0.06038217 0.98944288],0.0338380060805389,2,49,9,0.0005479014756450962,0.00010722378802376876
+1996,DO,[0.71868136 0.28131864 0.03107714 0.01326406 0.05924157 0.99683995],1.6055350840324536,0.0,[0.71881416 0.28118584 0.02919336 0.02074347 0.06038217 0.98944288],0.0006426131383956054,2,49,10,0.12357449472302884,0.01796200909527795
+1997,GA Mann-Wald,[0.72813455 0.27186545 0.03033048 0.02063604 0.06129963 0.98970103],1.7135004170704633,0.035434712798317135,[0.71881416 0.28118584 0.02919336 0.02074347 0.06038217 0.98944288],0.0338380060805389,2,49,10,0.0016109651831706095,0.00045799650887213346
+1998,iLSHADE Mann-Wald,[0.73179054 0.2862845  0.02993042 0.02015333 0.05982566 0.98940405],1.4131068750284612,0.03235067904482059,[0.71881416 0.28118584 0.02919336 0.02074347 0.06038217 0.98944288],0.0338380060805389,2,49,10,0.0008203487741265114,0.000169077570805029
+1999,EM,[0.71885943 0.28114057 0.02953711 0.02057002 0.05960595 0.98869891],0.913058958016336,0.03330812380137573,[0.71881416 0.28118584 0.02919336 0.02074347 0.06038217 0.98944288],0.0338380060805389,2,49,10,0.0006258333088184909,0.00016707509473111044
+2000,DO,[0.69912866 0.30087134 0.00360432 0.04978458 0.02453175 0.54336784],1.627414333052002,1.061973239500369e-05,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.0007240132810304535,2,50,1,4.2061535785262345,0.29924951931667554
+2001,GA Mann-Wald,[0.69676757 0.30323243 0.00635389 0.04859889 0.01273691 0.54399138],1.5613919170573354,0.03850468917507355,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.04107649581010999,2,50,1,0.0009894759052779811,0.0003213088711815792
+2002,iLSHADE Mann-Wald,[0.70692705 0.30773366 0.00630348 0.0497064  0.01278965 0.54435181],1.6498457080451772,0.03750289126690462,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.04107649581010999,2,50,1,0.0012687019705528435,0.0003170706967613262
+2003,EM,[0.70035018 0.29964982 0.00641658 0.04902025 0.01279476 0.54397707],0.1690374999307096,0.03885558109128927,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.04107649581010999,2,50,1,0.0010149950254336737,0.00028718481990913067
+2004,DO,[0.69953283 0.30046717 0.00693097 0.04957284 0.02624608 0.54344469],1.6752508749486879,0.0,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.0007240132810304535,2,50,2,1.3754214324899536,0.24660295354756592
+2005,GA Mann-Wald,[0.7001203  0.2998797  0.00666916 0.04913245 0.01309319 0.54544207],1.69973412505351,0.04318851679844912,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.04107649581010999,2,50,2,0.005262750547443407,0.0014132139322270538
+2006,iLSHADE Mann-Wald,[0.70692707 0.30358391 0.00630348 0.0498016  0.01278965 0.54453193],1.346562625025399,0.03748890475313862,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.04107649581010999,2,50,2,0.0012813331580376897,0.00030620291636759925
+2007,EM,[0.70035018 0.29964982 0.00641658 0.04902025 0.01279476 0.54397707],0.12866024998947978,0.03885558109128927,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.04107649581010999,2,50,2,0.0012540919138692708,0.00032882211722366886
+2008,DO,[0.6987401  0.3012599  0.00474347 0.04950322 0.02461874 0.54373267],1.6241062090266496,3.2234079310044626e-05,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.0007240132810304535,2,50,3,2.35154153706644,0.26368882307817965
+2009,GA Mann-Wald,[0.69802309 0.30197691 0.00633734 0.04960389 0.01248658 0.54393442],1.796991541981697,0.04023698721494638,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.04107649581010999,2,50,3,0.0003387433460670941,0.00010377073028215361
+2010,iLSHADE Mann-Wald,[0.70692705 0.30771589 0.00630348 0.0497081  0.01278965 0.54435052],1.38133295904845,0.03750288925148649,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.04107649581010999,2,50,3,0.0014410650192826502,0.0003509935997044289
+2011,EM,[0.70035018 0.29964982 0.00641658 0.04902025 0.01279476 0.54397707],0.33408858301118016,0.03885558109128927,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.04107649581010999,2,50,3,0.001156962191095623,0.00034202506057820237
+2012,DO,[0.69983503 0.30016497 0.00655367 0.049474   0.02886303 0.54363835],1.44670774997212,1.4169040307082763e-05,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.0007240132810304535,2,50,4,2.1851687120884735,0.3122898464262652
+2013,GA Mann-Wald,[0.70044565 0.29955435 0.0062762  0.04924522 0.01272379 0.54477513],1.613461249973625,0.038097699927578575,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.04107649581010999,2,50,4,0.0012996984604600748,0.0004056094879254619
+2014,iLSHADE Mann-Wald,[0.70692705 0.30771105 0.00630348 0.04970856 0.01278965 0.54435016],1.3652979580219835,0.03750288929703806,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.04107649581010999,2,50,4,0.001211584541824795,0.00024260147339240557
+2015,EM,[0.70035018 0.29964982 0.00641658 0.04902025 0.01279476 0.54397707],0.11522308306302875,0.03885558109128927,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.04107649581010999,2,50,4,0.0009658207456331169,0.0003009903843372234
+2016,DO,[0.7013487  0.2986513  0.00732027 0.04947481 0.02234019 0.54380621],1.500502165989019,2.219107582710536e-05,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.0007240132810304535,2,50,5,0.6424208815522796,0.14523340681102964
+2017,GA Mann-Wald,[0.70519259 0.29480741 0.00640581 0.04905629 0.01291807 0.54300342],1.5987480420153588,0.03900150230692045,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.04107649581010999,2,50,5,0.0017326223135438745,0.0004364702890707664
+2018,iLSHADE Mann-Wald,[0.7069271  0.30771483 0.00630348 0.0497082  0.01278965 0.54435044],1.3096550409682095,0.037502889660233865,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.04107649581010999,2,50,5,0.0012657478134463496,0.00020146555806292032
+2019,EM,[0.70035018 0.29964982 0.00641658 0.04902025 0.01279476 0.54397707],0.81498545804061,0.03885558109128927,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.04107649581010999,2,50,5,0.001351317906920002,0.00028453489071569995
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+2021,GA Mann-Wald,[0.70764326 0.29235674 0.00644442 0.04887733 0.01275031 0.5437612 ],1.5595327500486746,0.03908345557314753,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.04107649581010999,2,50,6,0.0012290537181178784,0.0003197391691702226
+2022,iLSHADE Mann-Wald,[0.70692705 0.30770958 0.00630348 0.0497087  0.01278965 0.54435006],1.365413499996066,0.037502889318637544,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.04107649581010999,2,50,6,0.0013963838255426353,0.00030270749796278444
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+2026,iLSHADE Mann-Wald,[0.70692706 0.30770984 0.00630348 0.04970868 0.01278965 0.54435007],1.3718114999355748,0.03750288932404,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.04107649581010999,2,50,7,0.001496818372983988,0.00036269701922598754
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+2029,GA Mann-Wald,[0.71698365 0.28301635 0.00652223 0.04906941 0.01294515 0.54522759],1.8140453330706805,0.04139082850380066,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.04107649581010999,2,50,8,0.0036297740767858855,0.0008836356652836191
+2030,iLSHADE Mann-Wald,[0.70692705 0.30773917 0.00630348 0.04970588 0.01278965 0.54435221],1.4762725420296192,0.037502892487382866,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.04107649581010999,2,50,8,0.001266811207373486,0.00035685095502458765
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+2034,iLSHADE Mann-Wald,[0.70692705 0.3077146  0.00630348 0.04970822 0.01278965 0.54435042],1.3512824579374865,0.037502889267369624,[0.70016026 0.29983974 0.0063855  0.0501132  0.01251903 0.54161697],0.04107649581010999,2,50,9,0.0011865301006639406,0.00026741357057897186
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+2014,iLSHADE Mann-Wald,"[ 0.03753448  0.05259604  0.32270334  0.3103043   0.18737797  0.07269169
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+2018,iLSHADE Mann-Wald,"[ 0.04307724  0.04750207  0.3276343   0.29937716  0.18933207  0.07803119
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diff --git a/results/optimization_results.csv b/results/optimization_results.csv
new file mode 100644
index 0000000..1c5f210
--- /dev/null
+++ b/results/optimization_results.csv
@@ -0,0 +1,189528 @@
+,problem_id,solver,fitness_mean,fitness_std,fitness_values,success_rate,solutions
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+       0.34537039, 0.26257579, 0.20284795, 0.12991148, 0.05662709]))]"
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+       0.09763498, 0.04327061, 0.08798371, 0.1890643 , 0.03561743,
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+       0.34019093, 0.36169169, 0.44285689, 0.53676104, 0.60370323,
+       0.66026423, 0.9691327 ]), scaled_genome=None, log_likelihood=11073.338130413707, fitness_values=array([5.14335686, 2.74659002, 1.79533092, 1.28902568, 0.90895172,
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+       0.3486685 , 0.36117874, 0.44285122, 0.53686336, 0.60288945,
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+       0.55553769, 0.42125769, 0.26848729, 0.1555819 , 0.08626511]))]"
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+       2.85487614, 2.4792231 , 1.74582357, 1.24205172, 1.24205172]))]"
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+       0.97130507, 0.74013174, 0.68426739, 0.56629159, 0.50415241]))]"
+3,synthetic_9_90,GASolver,0.9954225951019678,0.23340500080270551,"[0.87825742 0.65948076 1.11883561 1.10050126 1.15451884 1.25382452
+ 1.3162906  1.06791429 0.60283424 0.80176841]",0.0,"[Solution(fitness=0.878257419855636, genome=array([0.22068535, 0.04623488, 0.03717125, 0.10933179, 0.03567717,
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+       0.18847673, 0.37828227, 0.57875886, 0.62917982, 0.83951569,
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+       0.86218198, 0.63190122, 0.48038192, 0.3666203 , 0.28679174]))]"
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+       0.34830828, 0.41701174, 0.5333269 , 0.58016722, 0.70584699,
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+       0.35255836, 0.28335772, 0.23566543, 0.19057049, 0.16814227]))]"
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+       0.11092262, 0.1673491 , 0.11612855, 0.13997086, 0.00383504,
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+       0.23977948, 0.30623541, 0.44470742, 0.57550794, 0.67644547,
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+       0.44635078, 0.28689739, 0.14801534, 0.07380214, 0.04455295]))]"
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+       0.0564987 , 0.15049739, 0.04153336, 0.18729538, 0.00556774,
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+       0.24185778, 0.3535469 , 0.47783536, 0.60219351, 0.6755519 ,
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+       0.41157886, 0.38206427, 0.29999001, 0.2336202 , 0.16155568]))]"
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+       0.34524126, 0.45205861, 0.57141109, 0.67094702, 0.86174553,
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+       0.70934951, 0.49514139, 0.36170543, 0.26536565, 0.20856916]))]"
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+       0.48728778, 0.37789809, 0.30454055, 0.27041511, 0.2550794 ]))]"
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+       1.8208249 , 1.8208249 , 1.41916175, 1.41916175, 1.41916175]))]"
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+       0.34498666, 0.44673292, 0.56865064, 0.77244685, 0.91215222,
+       0.99326984, 1.1411396 ]), scaled_genome=None, log_likelihood=4821.664910376887, fitness_values=array([3.91447165, 3.61864271, 3.37187764, 2.60439895, 2.091102  ,
+       1.42244353, 1.42244353, 1.42244353, 1.13553723, 1.13553723])), Solution(fitness=1.1253927382333375, genome=array([0.1010485 , 0.27171242, 0.15985416, 0.07948339, 0.30102114,
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+       1.02748596, 1.2735138 ]), scaled_genome=None, log_likelihood=4233.600318232966, fitness_values=array([4.07968134, 2.66141736, 2.47756959, 2.47756959, 2.07660371,
+       1.16121087, 1.16121087, 1.12539274, 1.12539274, 1.12539274])), Solution(fitness=1.1927956301699374, genome=array([0.16309649, 0.29683226, 0.31391212, 0.1065278 , 0.0589019 ,
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+       0.02085861, 0.04845004, 0.03467767, 0.03685672, 0.04161712,
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+       0.31110649, 0.47042454, 0.59639957, 0.80669608, 0.91185964,
+       1.001728  , 1.11703528]), scaled_genome=None, log_likelihood=4093.6309406061287, fitness_values=array([3.87964394, 2.51074436, 2.51074436, 2.22226519, 2.22226519,
+       1.9904468 , 1.9904468 , 1.9904468 , 1.86990411, 1.19279563])), Solution(fitness=1.2175134821401286, genome=array([0.12567103, 0.36994269, 0.12702381, 0.14208627, 0.05240862,
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+       0.06747548, 0.01692966, 0.0305672 , 0.06589294, 0.16976501,
+       0.29604993, 0.43881146, 0.54798796, 0.77145343, 0.85907042,
+       1.00262063, 1.2225734 ]), scaled_genome=None, log_likelihood=3781.1666463043384, fitness_values=array([3.01529963, 3.01529963, 2.12817424, 2.07440054, 1.57331015,
+       1.57331015, 1.57331015, 1.47269645, 1.21751348, 1.21751348])), Solution(fitness=1.4285253025728784, genome=array([0.13756462, 0.17054383, 0.12288228, 0.10867062, 0.16706957,
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+       0.28960135, 0.47746022, 0.57742538, 0.70256726, 0.81764389,
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+       1.63015294, 1.63015294, 1.4285253 , 1.4285253 , 1.4285253 ])), Solution(fitness=1.2746001494302202, genome=array([0.17672033, 0.16417183, 0.26560726, 0.07866799, 0.2682318 ,
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+       0.018402  , 0.04868648, 0.02133323, 0.11505566, 0.15476094,
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+       2.09496307, 1.27460015, 1.27460015, 1.27460015, 1.27460015])), Solution(fitness=1.1074338258742198, genome=array([0.07851514, 0.34216665, 0.39307532, 0.20329773, 0.03      ,
+       0.14296406, 0.20674358, 0.15574501, 0.27262405, 0.08744845,
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+        0.0645829 ,  0.02830047,  0.02624074,  0.09407301,  0.01971058,
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+        1.09062929,  1.15873496]), scaled_genome=None, log_likelihood=5068.328032980801, fitness_values=array([3.4696647 , 3.4696647 , 3.28416821, 1.95943335, 1.64882721,
+       1.64882721, 1.51749481, 1.43009738, 1.3829338 , 1.38275171])), Solution(fitness=1.4295862750464916, genome=array([0.39110855, 0.42883514, 0.04980847, 0.18448481, 0.05276153,
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+       0.35076222, 0.43346544, 0.65848614, 0.76757401, 0.86869743,
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+       1.97982593, 1.6817343 , 1.6817343 , 1.6817343 , 1.42958628]))]"
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+        0.24561805,  0.46295357,  0.54205647,  0.6690876 ,  0.86871955,
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+       0.99835711, 1.1993442 ]), scaled_genome=None, log_likelihood=3410.7898991847997, fitness_values=array([3.32269264, 2.26963816, 1.87756901, 1.32196368, 1.32196368,
+       1.32196368, 1.32196368, 1.31702235, 1.15646452, 1.15646452])), Solution(fitness=1.5326482135108903, genome=array([0.19000194, 0.15294741, 0.03      , 0.22843743, 0.35240302,
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+       1.10833202, 1.20960986]), scaled_genome=None, log_likelihood=3846.4053796283506, fitness_values=array([2.90443035, 2.61766368, 1.99932752, 1.96619662, 1.96619662,
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+       0.45291304, 0.52464459, 0.63037515, 0.78170024, 0.85257712,
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+       0.33191756, 0.23881112, 0.18184841, 0.14990772, 0.13401513]))]"
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+       1.30639808, 1.30639808, 1.30639808, 1.29520645, 0.81770017]))]"
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+       0.28076121, 0.20863123, 0.1764822 , 0.15360178, 0.12409447]))]"
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+       1.18082806, 1.06185215, 0.97382043, 0.92985586, 0.90383576]))]"
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+       0.27783434, 0.22067743, 0.16571628, 0.13721948, 0.12191832]))]"
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+       0.28019587, 0.41146738, 0.53075213, 0.68428514, 0.71385339,
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+       0.30533935, 0.45215048, 0.58177536, 0.63426158, 0.82429119,
+       0.90739376, 1.0059635 ]), scaled_genome=None, log_likelihood=3021.5616110397973, fitness_values=array([1.2178444 , 0.55588983, 0.41981935, 0.3389549 , 0.23336257,
+       0.18136733, 0.1444833 , 0.11917371, 0.1047496 , 0.09616368])), Solution(fitness=0.09268604999275151, genome=array([0.1617875 , 0.28215554, 0.10310301, 0.216243  , 0.04942437,
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+       0.30684267, 0.45581296, 0.57769506, 0.62187606, 0.8236842 ,
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+       0.15147986, 0.12016415, 0.10545006, 0.09517434, 0.09268605]))]"
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+       0.19374194, 0.11830811, 0.07901012, 0.0374123 , 0.02518396]))]"
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+       0.30770717, 0.27705799, 0.23269663, 0.21124536, 0.20602424]))]"
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+       0.31484059, 0.36856913, 0.42543992, 0.60763488, 0.640865  ,
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+       0.40999243, 0.48924555, 0.50532755, 0.67428675, 0.7920341 ,
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+       0.40719837, 0.50280499, 0.6657537 , 0.78240116, 0.88134823,
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+        0.03912501,  0.01458893,  0.05362203,  0.02203775,  0.03272083,
+        0.01985833,  0.03150398,  0.03100737, -0.02025282,  0.11319544,
+        0.2699472 ,  0.40474655,  0.54523724,  0.65583811,  0.76194602,
+        0.90399142,  0.9732086 ]), scaled_genome=None, log_likelihood=4727.026848241396, fitness_values=array([4.35931011, 3.69796969, 3.18481799, 2.36980396, 1.5535001 ,
+       1.5535001 , 1.5535001 , 1.5535001 , 1.45025316, 1.45025316])), Solution(fitness=1.695907952091735, genome=array([ 0.05772998,  0.13463564,  0.11993191,  0.07149938,  0.21179346,
+        0.20529048,  0.03736553,  0.30668221,  0.25920829,  0.02388898,
+        0.05143705,  0.0078509 ,  0.0499856 ,  0.02355818,  0.01639746,
+        0.0086466 ,  0.01829149,  0.04664222, -0.03424751,  0.0530128 ,
+        0.22187075,  0.35925031,  0.56456101,  0.66911396,  0.78983216,
+        0.88446874,  0.95630156]), scaled_genome=None, log_likelihood=3287.389130564873, fitness_values=array([2.12977335, 2.12977335, 2.12977335, 1.97803089, 1.97803089,
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+       0.05564667, 0.04259718, 0.02206345, 0.07798947, 0.01294772,
+       0.04957673, 0.01494492, 0.05235422, 0.01695313, 0.08813118,
+       0.2699835 , 0.385104  , 0.54803901, 0.6530515 , 0.77139676,
+       0.9438162 , 1.01912598]), scaled_genome=None, log_likelihood=7511.538082308286, fitness_values=array([3.22529913, 1.6458213 , 1.6458213 , 1.6458213 , 1.6458213 ,
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+       0.24032909, 0.31820551, 0.51783387, 0.66472901, 0.74068488,
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+       0.31787215, 0.41790321, 0.52875152, 0.64340975, 0.8048198 ,
+       0.93747481, 1.03225586]), scaled_genome=None, log_likelihood=6216.089303884317, fitness_values=array([3.34564131, 2.70477642, 2.70477642, 2.24614951, 1.9618989 ,
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+       0.90415457, 0.97072002]), scaled_genome=None, log_likelihood=1988.868413860465, fitness_values=array([4.07680555, 3.20743724, 2.31481344, 2.02849646, 2.02849646,
+       1.6898564 , 1.6898564 , 1.6898564 , 1.6898564 , 1.6898564 ])), Solution(fitness=1.5948034717637702, genome=array([0.18906662, 0.14572017, 0.05891413, 0.08840996, 0.03      ,
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+       0.29189663, 0.3748494 , 0.5611047 , 0.65173886, 0.7144832 ,
+       0.96310736, 1.0435347 ]), scaled_genome=None, log_likelihood=4731.858669895654, fitness_values=array([2.25409064, 2.25409064, 2.10079417, 2.10079417, 1.62546173,
+       1.62546173, 1.62546173, 1.62546173, 1.62546173, 1.62546173])), Solution(fitness=1.1863571471650514, genome=array([0.15471423, 0.03      , 0.12844415, 0.19234357, 0.2131018 ,
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+       1.19588178, 1.19588178, 1.18635715, 1.18635715, 1.18635715]))]"
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+       0.34704948, 0.44325438, 0.54219574, 0.65744261, 0.75013839,
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+       0.41114335, 0.37554197, 0.35254872, 0.28464815, 0.23814655]))]"
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+       0.39028407, 0.4186585 , 0.52982354, 0.67581699, 0.74985619,
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+       0.36410234, 0.31818905, 0.26033669, 0.21814416, 0.20342491]))]"
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+       0.21417341, 0.2903094 , 0.37770706, 0.56559417, 0.64177276,
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+       0.1615884 , 0.29020206, 0.37886133, 0.5644019 , 0.65281782,
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+       0.06632319, 0.045007  , 0.01262829, 0.0372984 , 0.01852   ,
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+       0.03      , 0.06281424, 0.22904037, 0.07278423, 0.02445927,
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+       0.30838957, 0.36398614, 0.58018272, 0.68869658, 0.79757558,
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+       6.96093790e-01, 8.17394116e-01, 9.59922923e-01]), scaled_genome=None, log_likelihood=5479.034908360455, fitness_values=array([4.28987143, 2.26412196, 2.26412196, 2.26412196, 1.67084169,
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+        0.18079157,  0.25553489,  0.3936447 ,  0.60486936,  0.68115151,
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+       1.09471942, 1.09471942, 1.09471942, 1.09471942, 0.96665695]))]"
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+       0.31106844, 0.39649623, 0.56922309, 0.63559266, 0.783216  ,
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+       1.74713699, 1.74713699, 1.74713699, 1.2108725 , 1.2108725 ]))]"
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+       0.2862073 , 0.4482994 , 0.54053429, 0.627965  , 0.77533129,
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+       0.36363268, 0.50705098, 0.6186649 , 0.82595453, 0.90486377,
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+       0.36537141, 0.2600412 , 0.18197939, 0.10801034, 0.08159616]))]"
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+       0.37020797, 0.43151781, 0.55342848, 0.64074055, 0.69100673,
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+       0.3770488 , 0.43488261, 0.56330006, 0.63645177, 0.68443873,
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+       0.02909505, 0.02114557, 0.0241876 , 0.03742183, 0.02840607,
+       0.01869684, 0.02602017, 0.05469358, 0.16126052, 0.21151721,
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+       0.37235218, 0.43472998, 0.57898164, 0.63503543, 0.68906346,
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+       0.37702864, 0.43075531, 0.5658224 , 0.6455522 , 0.68992067,
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+       0.35472695, 0.49049121, 0.54554292, 0.66357845, 0.75811668,
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+       0.7092724 , 0.58548585, 0.48940198, 0.37235951, 0.34378573]))]"
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+       0.35671279, 0.51930718, 0.58489436, 0.73137753, 0.88023541,
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+       0.04512995, 0.03873815, 0.03677551, 0.01010755, 0.024634  ,
+       0.06467581, 0.01317398, 0.0567913 , 0.018926  , 0.19090329,
+       0.315116  , 0.47724133, 0.52498725, 0.6644805 , 0.86654281,
+       0.96537512, 1.08244144]), scaled_genome=None, log_likelihood=8972.79577343745, fitness_values=array([4.21198656, 3.19826223, 2.16819899, 1.75082346, 1.22515007,
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+       1.04495545, 0.7719296 , 0.63897389, 0.50418022, 0.4404322 ])), Solution(fitness=0.45459393429659606, genome=array([0.3552535 , 0.07425464, 0.04739615, 0.09812773, 0.22307898,
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+       0.26662004, 0.23119617, 0.19989142, 0.15696225, 0.13318379])), Solution(fitness=0.12847872052986592, genome=array([0.08877483, 0.07814361, 0.16607198, 0.0714292 , 0.10878658,
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+       0.04104437, 0.01708136, 0.03994025, 0.02663058, 0.01692726,
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+       0.33885601, 0.42430605, 0.55388986, 0.60502641, 0.75844908,
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+       0.0435947 , 0.02582082, 0.03209279, 0.02736485, 0.0200033 ,
+       0.00875019, 0.03750249, 0.0458768 , 0.10583951, 0.19239751,
+       0.34028248, 0.41649091, 0.5578755 , 0.607306  , 0.75849994,
+       0.85729114, 0.98128645]), scaled_genome=None, log_likelihood=4053.6890398860774, fitness_values=array([1.12782611, 0.67116147, 0.48009125, 0.39343498, 0.38222979,
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+       0.0228398 , 0.03571736, 0.03098655, 0.03838972, 0.00997635,
+       0.03471356, 0.03378324, 0.02683976, 0.10550191, 0.25570521,
+       0.38973756, 0.47806921, 0.57485646, 0.68714694, 0.75905249,
+       0.87683079, 0.98521119]), scaled_genome=None, log_likelihood=8962.011096079274, fitness_values=array([2.29098265, 1.2261862 , 1.01494293, 0.67807988, 0.56409957,
+       0.48322575, 0.36251626, 0.29882795, 0.2590296 , 0.23171922])), Solution(fitness=0.24162721162405776, genome=array([0.15496191, 0.12420364, 0.04093273, 0.04416703, 0.26916345,
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+       0.3445968 , 0.46824897, 0.58048095, 0.68783722, 0.75022812,
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+       0.34421616, 0.49097359, 0.57592261, 0.68933094, 0.80463113,
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+       0.02936115, 0.00717844, 0.02930411, 0.04146   , 0.00963063,
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+       0.40485858, 0.49041182, 0.58680135, 0.68796486, 0.81272819,
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+       0.36352084, 0.49027439, 0.57771714, 0.68856772, 0.7819224 ,
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+       0.44534834, 0.36140588, 0.30194951, 0.26414757, 0.19896642]))]"
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+       1.000636  , 0.8755265 , 0.7060997 , 0.64246976, 0.61358038])), Solution(fitness=0.4418049284740204, genome=array([0.1745233 , 0.06320565, 0.06158326, 0.09659925, 0.13843204,
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+       0.4248798 , 0.48534209, 0.72314595, 0.76309705, 0.87411662,
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+       0.21688097, 0.39419013, 0.53123602, 0.72650445, 0.89108749,
+       1.04259554]), scaled_genome=None, log_likelihood=524.3162006220161, fitness_values=array([0.36883322, 0.36883322, 0.21201527, 0.12958179, 0.09891706,
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+       1.0434286 ]), scaled_genome=None, log_likelihood=566.6899871382542, fitness_values=array([0.50603587, 0.30330831, 0.22588952, 0.12451598, 0.08107857,
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+       1.0227227 ]), scaled_genome=None, log_likelihood=384.19373019662135, fitness_values=array([0.44730013, 0.27791774, 0.18416437, 0.11827482, 0.07021151,
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+       1.04578486]), scaled_genome=None, log_likelihood=511.46583970790675, fitness_values=array([0.48874761, 0.19899234, 0.16602692, 0.0986373 , 0.07983014,
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+       0.29667743, 0.46611638, 0.59248733, 0.73302447, 0.85429351,
+       1.08539344]), scaled_genome=None, log_likelihood=7429.216271758065, fitness_values=array([2.17998888, 1.85383756, 1.85383756, 1.38913484, 1.17168506,
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+       0.27431445, 0.44493831, 0.59092187, 0.73387834, 0.89270146,
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+       1.06942399, 0.75152575, 0.75152575, 0.75152575, 0.75152575]))]"
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+       1.02237982]), scaled_genome=None, log_likelihood=7412.154746260517, fitness_values=array([1.87244454, 1.31116361, 0.95105481, 0.5650806 , 0.36414775,
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+       1.03597063]), scaled_genome=None, log_likelihood=7779.571215432547, fitness_values=array([2.39971448, 1.14150375, 0.85286604, 0.65871891, 0.49164631,
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+       1.02295783]), scaled_genome=None, log_likelihood=7687.9902156238295, fitness_values=array([1.70170468, 1.35824948, 0.71076129, 0.59789605, 0.42161221,
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+       1.01967458]), scaled_genome=None, log_likelihood=3005.1384798733457, fitness_values=array([0.77230091, 0.49225322, 0.38130662, 0.25503763, 0.1912267 ,
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+       1.01197371e+00]), scaled_genome=None, log_likelihood=983.9886815893634, fitness_values=array([1.39165536, 0.90937214, 0.84541285, 0.63354058, 0.48951087,
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+       0.56268335, 0.10485256, 0.05862259, 0.0427266 , 0.03815599,
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+       0.0439056 , 0.0270529 , 0.06597992, 0.04923492, 0.05272214,
+       0.14301791, 0.30041456, 0.40662767, 0.60773425, 0.73987842,
+       0.88593938]), scaled_genome=None, log_likelihood=5996.266877790764, fitness_values=array([2.42488175, 1.88047634, 1.09273554, 0.45245635, 0.45245635,
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+       0.89596974]), scaled_genome=None, log_likelihood=6344.756490393158, fitness_values=array([2.7636537 , 1.42536222, 0.78396605, 0.78396605, 0.39706969,
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+       0.04880879, 0.02249922, 0.06549538, 0.04090364, 0.05309027,
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+       0.88916229]), scaled_genome=None, log_likelihood=6102.281978505308, fitness_values=array([3.01000437, 1.92462847, 0.91631323, 0.60971123, 0.38506698,
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+       0.88370221]), scaled_genome=None, log_likelihood=6297.180634904173, fitness_values=array([2.240928  , 1.55604658, 0.70132009, 0.55862506, 0.31071979,
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+       0.15585041, 0.34195672, 0.43786398, 0.62915092, 0.85980801,
+       0.9705381 ]), scaled_genome=None, log_likelihood=4757.382550388367, fitness_values=array([1.31781641, 1.13685876, 1.13685876, 1.13685876, 1.11212299,
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+       0.96442441]), scaled_genome=None, log_likelihood=3103.302282742902, fitness_values=array([2.23968445, 1.35917846, 1.35917846, 1.35917846, 1.23966126,
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+       0.98937257]), scaled_genome=None, log_likelihood=4748.218789383487, fitness_values=array([2.11480634, 1.64367873, 1.43104848, 1.18342212, 0.85160226,
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+       0.26278347, 0.38775464, 0.62960799, 0.76893524, 0.90423831,
+       0.9877468 ]), scaled_genome=None, log_likelihood=7132.158435918334, fitness_values=array([3.46898559, 2.06023607, 1.96597466, 1.76619652, 1.47523677,
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+       1.10137164]), scaled_genome=None, log_likelihood=6577.484960765621, fitness_values=array([1.1227169 , 0.72782598, 0.51322485, 0.31169184, 0.18128288,
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+       0.02626826, 0.01386595, 0.06530741, 0.12856205, 0.08742154,
+       0.37529236, 0.4253144 , 0.68401939, 0.79635833, 0.9431572 ,
+       1.245012  ]), scaled_genome=None, log_likelihood=6624.016477479145, fitness_values=array([2.25086571, 0.9457947 , 0.55821144, 0.44578658, 0.22562695,
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+       1.24492551]), scaled_genome=None, log_likelihood=6599.753133063523, fitness_values=array([1.64078159, 0.88916936, 0.53024058, 0.38219399, 0.21368801,
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+       0.37567236, 0.4253357 , 0.6841891 , 0.79637029, 0.9446609 ,
+       1.24499167]), scaled_genome=None, log_likelihood=6576.556450608492, fitness_values=array([1.55443461, 1.00883684, 0.58215301, 0.33384453, 0.19022662,
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+       1.13718247]), scaled_genome=None, log_likelihood=6597.464847592936, fitness_values=array([1.85174902, 1.0666631 , 0.6438798 , 0.35555689, 0.24444285,
+       0.13497397, 0.089586  , 0.07110812, 0.0663959 , 0.06531599]))]"
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+        0.2269469 ,  0.41923257,  0.71674799,  0.79305959,  1.0077298 ,
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+       0.0322011 , 0.02636094, 0.05909887, 0.03289685, 0.082113  ,
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+       1.24354643]), scaled_genome=None, log_likelihood=5968.474124539747, fitness_values=array([2.32586483, 1.58946711, 1.15457623, 1.11418361, 0.8793227 ,
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+        0.02672438,  0.01141229,  0.10226   ,  0.03438815, -0.0406266 ,
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+        1.20999775]), scaled_genome=None, log_likelihood=5472.902108092074, fitness_values=array([2.28783623, 2.22946459, 1.18084677, 1.18084677, 1.18084677,
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+       0.98871359]), scaled_genome=None, log_likelihood=6344.75781448877, fitness_values=array([1.6940509 , 0.84720606, 0.67137127, 0.43598724, 0.37471805,
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+       0.27102526, 0.25816456, 0.23340232, 0.233037  , 0.23138965])), Solution(fitness=0.2143608405968798, genome=array([0.27701293, 0.10811651, 0.10853337, 0.16886181, 0.05415878,
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+       0.98189124]), scaled_genome=None, log_likelihood=2754.309631894066, fitness_values=array([0.81476349, 0.51885229, 0.26401152, 0.1810761 , 0.09248685,
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+       0.98251706]), scaled_genome=None, log_likelihood=2800.947745573758, fitness_values=array([0.6680352 , 0.45082485, 0.27794374, 0.14130746, 0.09705805,
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+       0.98205292]), scaled_genome=None, log_likelihood=2818.427155478366, fitness_values=array([0.55825992, 0.31593577, 0.20862408, 0.11692855, 0.08401887,
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+       0.04107408, 0.02313592, 0.0170699 , 0.01493226, 0.01435475]))]"
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+        0.0229011 ,  0.02525579,  0.06972474,  0.04509845, -0.00469557,
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+        1.0164444 ]), scaled_genome=None, log_likelihood=3522.4136465663123, fitness_values=array([1.35909695, 0.90777472, 0.58670548, 0.51717904, 0.51717904,
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+       0.03704942, 0.05507744, 0.05315761, 0.0318044 , 0.04409685,
+       0.1826839 , 0.37551864, 0.42769346, 0.599339  , 0.77287622,
+       0.96638958]), scaled_genome=None, log_likelihood=2506.0569034760974, fitness_values=array([0.52911924, 0.52911924, 0.45296313, 0.45296313, 0.43580659,
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+       0.03262842, 0.07551334, 0.0370085 , 0.09431093, 0.06990525,
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+       1.04578876]), scaled_genome=None, log_likelihood=1838.9054935013396, fitness_values=array([0.92861315, 0.92861315, 0.8216236 , 0.65031616, 0.48997836,
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+       0.2601566 , 0.38410715, 0.49130479, 0.72097793, 0.80840867,
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+       0.98389249]), scaled_genome=None, log_likelihood=1992.7304423121732, fitness_values=array([0.42262065, 0.35651064, 0.20699911, 0.1478566 , 0.09140998,
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+       0.22480432, 0.39212527, 0.58180173, 0.74373089, 0.79761822,
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+       0.16242131, 0.28548423, 0.49090823, 0.53330724, 0.71483698,
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+        0.86591429]), scaled_genome=None, log_likelihood=6566.711682683254, fitness_values=array([3.57389175, 1.82878432, 1.44273781, 1.44273781, 1.21065908,
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+        0.96687609]), scaled_genome=None, log_likelihood=6603.869361505467, fitness_values=array([2.65737072, 2.08688544, 1.18795525, 0.89279289, 0.89279289,
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+        0.92846474]), scaled_genome=None, log_likelihood=6358.468679768805, fitness_values=array([2.88947472, 0.99811919, 0.6349931 , 0.55782804, 0.41021848,
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+        0.99029928]), scaled_genome=None, log_likelihood=6362.880926324804, fitness_values=array([3.71540922, 1.67105653, 1.24242904, 1.08241654, 1.08241654,
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+       0.24878038, 0.50569353, 0.57184133, 0.76280806, 0.97726407,
+       1.24172904]), scaled_genome=None, log_likelihood=4968.289166241584, fitness_values=array([1.01583181, 1.00839523, 1.00839523, 0.59022577, 0.59022577,
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+       1.13075846e+00]), scaled_genome=None, log_likelihood=2604.58882001689, fitness_values=array([1.67467503, 1.3588843 , 1.30672436, 0.62483229, 0.62483229,
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+       0.71299631, 0.92207798, 1.08853081]), scaled_genome=None, log_likelihood=10625.45966952796, fitness_values=array([3.98230074, 2.54135457, 1.21609643, 1.21609643, 0.85972708,
+       0.83129223, 0.62975416, 0.62975416, 0.58508111, 0.48923333]))]"
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+       0.25117714, 0.11551459, 0.06729799, 0.04415768, 0.03871352])), Solution(fitness=0.021889860082077335, genome=array([0.03611202, 0.2790151 , 0.12257674, 0.44887309, 0.04159904,
+       0.09954787, 0.05520438, 0.05261081, 0.05616942, 0.00272367,
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+       0.04288771, 0.0468295 , 0.21829827, 0.29129337, 0.53577238,
+       0.60887211, 0.74745017, 0.9783641 ]), scaled_genome=None, log_likelihood=3908.9553220844664, fitness_values=array([1.0303652 , 0.42309975, 0.22513252, 0.11927557, 0.05254575,
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+       0.20526765, 0.17972112, 0.14959604, 0.13704653, 0.13142065]))]"
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+       0.43626437, 0.69796961, 0.97364696]), scaled_genome=None, log_likelihood=2887.3619357257435, fitness_values=array([1.45369782, 0.73967124, 0.58788499, 0.36572133, 0.25894641,
+       0.19437584, 0.15580275, 0.13819928, 0.13480679, 0.1345609 ])), Solution(fitness=0.13480007608236425, genome=array([0.03536165, 0.04555954, 0.1272149 , 0.12480795, 0.58307929,
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+       0.07975117, 0.03994676, 0.01781493, 0.15656266, 0.28371203,
+       0.43644446, 0.69825102, 0.97378598]), scaled_genome=None, log_likelihood=2871.11705104651, fitness_values=array([1.20728487, 0.35223156, 0.32435602, 0.27988019, 0.21095799,
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+       0.07947777, 0.03998544, 0.02797376, 0.16539894, 0.28373873,
+       0.4367955 , 0.69808301, 0.97342132]), scaled_genome=None, log_likelihood=2893.5227057644634, fitness_values=array([1.12626696, 0.77134317, 0.48030167, 0.3502922 , 0.23962831,
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+       0.00985075, 0.03980837, 0.00780702, 0.1440994 , 0.28367042,
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+       0.19400554, 0.17613351, 0.15738445, 0.1439869 , 0.14290934])), Solution(fitness=0.13464503895194604, genome=array([0.03993442, 0.04035194, 0.12641737, 0.12578176, 0.58589741,
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+       0.07955044, 0.03984919, 0.026155  , 0.16574664, 0.2837156 ,
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+       0.00976009, 0.04027281, 0.03664726, 0.17041203, 0.28363869,
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+       0.07971992, 0.03991785, 0.03771882, 0.17381994, 0.2837306 ,
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+       0.15229088, 0.13713409, 0.13479228, 0.13453113, 0.13449878]))]"
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+        0.17000706,  0.04637487,  0.05570619,  0.00300986,  0.08645382,
+        0.05817154,  0.04850317, -0.00651157,  0.12855899,  0.28331431,
+        0.49142909,  0.71414711,  0.96860232]), scaled_genome=None, log_likelihood=2991.2348078082446, fitness_values=array([1.78389112, 0.99478458, 0.55832715, 0.46871804, 0.35513024,
+       0.29824417, 0.24841824, 0.20321864, 0.19260821, 0.18260033])), Solution(fitness=0.17819864651991146, genome=array([0.03      , 0.04275986, 0.1343282 , 0.25295516, 0.47891496,
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+       0.30109121, 0.22480913, 0.19898529, 0.19123233, 0.17819865]))]"
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+       0.05971025, 0.04466144, 0.01304906, 0.15450231, 0.28433266,
+       0.4975322 , 0.71679634, 0.96821591]), scaled_genome=None, log_likelihood=2237.999800712917, fitness_values=array([1.03994812, 0.6215601 , 0.49893608, 0.43703541, 0.26368975,
+       0.23617434, 0.22341725, 0.21906664, 0.21341296, 0.21220414])), Solution(fitness=0.21750563155098449, genome=array([0.02380319, 0.05153729, 0.13345453, 0.25743826, 0.38950167,
+       0.14426506, 0.04887623, 0.06927882, 0.00454269, 0.09296971,
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+       0.07562201, 0.04409594, 0.00333966, 0.18426422, 0.28312247,
+       0.4483544 , 0.70217679, 0.9661216 ]), scaled_genome=None, log_likelihood=2383.530930993966, fitness_values=array([0.87071838, 0.7073561 , 0.56334418, 0.44507691, 0.28212726,
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+       0.07911713, 0.14216068, 0.05262535, 0.17704384, 0.44142853,
+       0.72300422, 0.9250834 , 1.20770123]), scaled_genome=None, log_likelihood=3012.3836663932784, fitness_values=array([1.32517345, 0.99553116, 0.78151173, 0.696238  , 0.696238  ,
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+       0.06815021, 0.08256868, 0.01909756, 0.20853085, 0.42818664,
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+       0.11177164, 0.118382  , 0.0534346 , 0.38690581, 0.46692748,
+       0.70925581, 0.91488507, 1.28194417]), scaled_genome=None, log_likelihood=5519.488739072832, fitness_values=array([1.24503498, 1.09399279, 1.07046547, 0.83917712, 0.59251208,
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+       0.05496514, 0.09639716, 0.02147902, 0.375879  , 0.48933387,
+       0.72370408, 0.89760609, 1.06026204]), scaled_genome=None, log_likelihood=3890.47602331978, fitness_values=array([1.05781006, 0.68472327, 0.68472327, 0.4921127 , 0.4921127 ,
+       0.4921127 , 0.4921127 , 0.45396351, 0.41553225, 0.35937986]))]"
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+        0.13272252,  0.05918551, -0.12998649,  0.0843281 ,  0.42909703,
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+        0.08554894,  0.11947276, -0.15230619,  0.13004873,  0.42719313,
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+       0.40150565, 0.30970791, 0.30587731, 0.30587731, 0.29558666]))]"
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+       0.07410434, 0.10839615, 0.0472147 , 0.26215239, 0.46550558,
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+       0.06900158, 0.03493398, 0.03006459, 0.02850275, 0.02811895]))]"
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+       0.60159199, 0.48797163, 0.48797163, 0.37344981, 0.37344981]))]"
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+       0.05756512, 0.04157186, 0.12532649, 0.32097193, 0.53507963,
+       0.63519388, 0.82297534, 0.9273911 ]), scaled_genome=None, log_likelihood=2975.0410097964386, fitness_values=array([0.60038615, 0.60038615, 0.3384404 , 0.3384404 , 0.29253271,
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+       0.83026338, 0.93094111, 1.06664834]), scaled_genome=None, log_likelihood=3461.7041855862094, fitness_values=array([1.45044461, 1.01505189, 0.65846916, 0.42873607, 0.42873607,
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+       0.7532485 , 0.89291133, 0.96949346]), scaled_genome=None, log_likelihood=3097.3080492323706, fitness_values=array([1.45144231, 0.67692862, 0.57919244, 0.44776328, 0.44776328,
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+       0.65178351, 0.75507317, 0.92255177]), scaled_genome=None, log_likelihood=2876.503541344209, fitness_values=array([0.86910864, 0.57146007, 0.43109915, 0.43109915, 0.38884673,
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+       0.05894019, 0.0452074 , 0.12042289, 0.30703915, 0.44136864,
+       0.62271185, 0.81009192, 0.93125425]), scaled_genome=None, log_likelihood=2677.6644629578195, fitness_values=array([0.71943883, 0.71943883, 0.60755272, 0.41875767, 0.41875767,
+       0.41875767, 0.19984927, 0.19984927, 0.19984927, 0.19984927]))]"
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+       0.19858295, 0.16433608, 0.14108512, 0.12871802, 0.12704931]))]"
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+       0.03413917, 0.02984053, 0.02867551, 0.02826752, 0.0281936 ]))]"
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+       1.40486138, 1.15033857, 0.9902713 , 0.9902713 , 0.89951121]))]"
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+       0.04722065, 0.0169683 , 0.11523621, 0.299631  , 0.42568655,
+       0.676666  , 0.79347098, 0.98667347]), scaled_genome=None, log_likelihood=5976.11802322712, fitness_values=array([3.29587451, 2.21000718, 1.59515993, 1.31008864, 1.27305683,
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+       0.70368094, 0.814609  , 0.97694655]), scaled_genome=None, log_likelihood=5118.1247053785955, fitness_values=array([2.18514548, 1.68557707, 1.05802092, 0.77522465, 0.65289416,
+       0.65289416, 0.63890896, 0.62551303, 0.60316425, 0.57359661]))]"
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+       0.05561848, 0.07412498, 0.05948351, 0.24645425, 0.3159992 ,
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+       0.04816145, 0.07600683, 0.0590158 , 0.24658612, 0.31611959,
+       0.68570022, 0.79146665, 0.90071708]), scaled_genome=None, log_likelihood=6853.465936705273, fitness_values=array([1.2261488 , 0.61140171, 0.38266004, 0.21882064, 0.13969209,
+       0.05549473, 0.01902996, 0.01541128, 0.01498738, 0.01484255]))]"
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+       0.64894326, 0.80075979, 1.00371398]), scaled_genome=None, log_likelihood=6172.281458042502, fitness_values=array([1.84957335, 1.16275534, 0.92953252, 0.59931381, 0.59931381,
+       0.59931381, 0.59931381, 0.5807474 , 0.42000576, 0.42000576]))]"
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+       0.49082737, 0.48179696, 0.45918709, 0.45460027, 0.45146121])), Solution(fitness=0.47066388898370637, genome=array([0.047446  , 0.62356904, 0.03958362, 0.06264969, 0.18420656,
+       0.04254508, 0.04285943, 0.03122031, 0.10515737, 0.04010077,
+       0.04855631, 0.05253902, 0.06440106, 0.25541242, 0.36754649,
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+       0.18269596, 0.05108556, 0.04364183, 0.05610194, 0.00501897,
+       0.022004  , 0.09105205, 0.12109347, 0.43371119, 0.63402378,
+       0.73096596, 0.84791354, 1.015483  ]), scaled_genome=None, log_likelihood=8174.852863826905, fitness_values=array([2.99462176, 2.06060203, 2.06060203, 0.9803297 , 0.9803297 ,
+       0.9803297 , 0.9803297 , 0.9360803 , 0.91376875, 0.59177728])), Solution(fitness=0.69333653798922, genome=array([0.66187869, 0.38032878, 0.18835296, 0.31375466, 0.28922499,
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+       0.08646452, 0.05500886, 0.11967183, 0.48591898, 0.60629041,
+       0.73551209, 0.79609843, 1.07096439]), scaled_genome=None, log_likelihood=6935.398350273159, fitness_values=array([3.4305206 , 2.93601347, 2.01744574, 1.2618469 , 1.2049761 ,
+       0.99695282, 0.94357422, 0.7906805 , 0.7906805 , 0.69333654])), Solution(fitness=0.5618892676813892, genome=array([0.50401798, 0.1059668 , 0.32284283, 0.26710157, 0.2155213 ,
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+       0.10009542, 0.03921552, 0.16777265, 0.40557985, 0.58184227,
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+       0.04356644, 0.03147396, 0.14618731, 0.34595208, 0.62159372,
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+       0.07482147, 0.02266127, 0.14269078, 0.43495214, 0.62682222,
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+       0.03868074, 0.10009542, 0.15022363, 0.40285207, 0.62973735,
+       0.73350895, 0.89818141, 1.06706119]), scaled_genome=None, log_likelihood=7145.786522433396, fitness_values=array([6.36986137, 3.69115386, 1.96699899, 1.29246659, 1.27930932,
+       0.84373709, 0.79049008, 0.77548025, 0.68863668, 0.59050445]))]"
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+       0.29953959, 0.2303372 , 0.16553835, 0.12362   , 0.0913492 ]))]"
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+       0.36511575, 0.31457805, 0.26398168, 0.22390065, 0.1903387 ]))]"
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+       0.10367271, 0.09560899, 0.07547105, 0.06003233, 0.04720047]))]"
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+       0.10371375, 0.07231646, 0.05454258, 0.04951578, 0.04887661]))]"
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+       0.42685922, 0.35020021, 0.35020021, 0.35020021, 0.34526121]))]"
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+        0.39284548,  0.78027383,  0.99573821]), scaled_genome=None, log_likelihood=2637.741083872639, fitness_values=array([1.58296719, 0.8524217 , 0.8524217 , 0.63275035, 0.63275035,
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+        0.03923489,  0.02058144, -0.08523463,  0.09159655,  0.40451647,
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+        0.49071011,  0.82621333,  0.99505499]), scaled_genome=None, log_likelihood=1528.6975262063916, fitness_values=array([0.76653975, 0.76653975, 0.76653975, 0.57764255, 0.55018745,
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+        0.20355657,  0.05733533,  0.09661967,  0.03895087,  0.00132243,
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+        0.11687291,  0.03019993, -0.00716286,  0.100308  ,  0.32552169,
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+       0.42783389, 0.38662   , 0.32842544, 0.32842544, 0.32707678]))]"
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+        0.46671566,  0.83273004,  0.99687607]), scaled_genome=None, log_likelihood=1639.8402185766795, fitness_values=array([0.91522331, 0.54527972, 0.36574006, 0.28953217, 0.20782477,
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+        0.51397407,  0.822722  ,  0.99840269]), scaled_genome=None, log_likelihood=1673.5159795216407, fitness_values=array([0.72470576, 0.62363963, 0.37008772, 0.34859061, 0.26397077,
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+       8.32713565e-01, 1.00060228e+00]), scaled_genome=None, log_likelihood=1650.1375797073424, fitness_values=array([0.95402365, 0.40681017, 0.33244465, 0.2612232 , 0.18234994,
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+       0.37677007, 0.36793253, 0.33523937, 0.29743147, 0.27185734]))]"
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+       0.19983483, 0.12988993, 0.09610264, 0.06947194, 0.0647587 ])), Solution(fitness=0.016914773330405898, genome=array([0.19669426, 0.05469812, 0.16708737, 0.04747451, 0.32269396,
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+       0.0551158 , 0.0428868 , 0.07721424, 0.1192385 , 0.32224525,
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+       0.17667747, 0.10168257, 0.04424515, 0.035124  , 0.0343112 ])), Solution(fitness=0.03538630052308038, genome=array([0.10713702, 0.13453252, 0.20973798, 0.04513813, 0.03069766,
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+       0.09063034, 0.06083485, 0.0451038 , 0.03742394, 0.02771043]))]"
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+       0.22388865, 0.20358503, 0.1787986 , 0.16089492, 0.10051708]))]"
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+       0.5502814 , 0.63982654, 0.8147703 ]), scaled_genome=None, log_likelihood=5031.657653547361, fitness_values=array([1.1921117 , 0.9141401 , 0.9141401 , 0.65252598, 0.65252598,
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+       0.01593854, 0.08232768, 0.12835666, 0.21572637, 0.31481014,
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+        0.01361651,  0.07086168, -0.14718144,  0.1440756 ,  0.21990471,
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+       0.00854895, 0.06441433, 0.07189845, 0.192439  , 0.27458409,
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+        0.02076341,  0.06083827, -0.01120749,  0.21942472,  0.35266334,
+        0.57253197,  0.63824847,  0.79466238]), scaled_genome=None, log_likelihood=3947.7866356961345, fitness_values=array([1.43982147, 1.30275825, 0.63787293, 0.63787293, 0.63787293,
+       0.56725552, 0.50935193, 0.50159077, 0.50159077, 0.50159077]))]"
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+       0.19772331, 0.06776872, 0.03414465, 0.02163289, 0.01888751]))]"
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+       0.9662705 , 0.81592663, 0.78182625, 0.73589939, 0.69668785]))]"
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+       0.1823668 , 0.12889226, 0.11715511, 0.08551149, 0.06108838]))]"
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+       0.03190225, 0.02894586, 0.43654146, 0.46872133, 0.61211613,
+       0.65576373, 0.70097903, 0.77750916]), scaled_genome=None, log_likelihood=6186.214035826256, fitness_values=array([0.40197806, 0.15254174, 0.10084334, 0.07221811, 0.04850908,
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+       0.02729185, 0.03050614, 0.43624526, 0.4681177 , 0.61249736,
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+       0.24520115, 0.024079  , 0.02182775, 0.03071342, 0.00828319,
+       0.0283654 , 0.03006957, 0.43607331, 0.46819056, 0.61323218,
+       0.65570516, 0.699753  , 0.77511661]), scaled_genome=None, log_likelihood=6192.070450377651, fitness_values=array([0.28685986, 0.15487015, 0.08244846, 0.05414662, 0.04290795,
+       0.03579934, 0.02542634, 0.02315166, 0.02220569, 0.02210726]))]"
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+       0.64512627, 0.72014405, 0.7814746 ]), scaled_genome=None, log_likelihood=7025.6483295498365, fitness_values=array([0.62851598, 0.39210737, 0.27919618, 0.24697625, 0.24697625,
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+        0.00869849,  0.05518858, -0.03880345,  0.11476536,  0.29492538,
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+       0.59157488, 0.59157488, 0.57226698, 0.57226698, 0.51399675]))]"
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+       0.49657984, 0.41665217, 0.36703157, 0.34261281, 0.34020797]))]"
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+       0.03223324, 0.01818807, 0.07250179, 0.16133109, 0.25991159,
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+        0.02771111,  0.07009523, -0.09792063,  0.06470791,  0.20608823,
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+       0.60861059, 0.48731946, 0.44759896, 0.35216736, 0.35216736]))]"
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+        1.59401011,  1.448864  ,  1.33949587,  1.22721505,  1.1904641 ]))]"
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+       0.99934593, 0.9715061 , 0.96157894, 0.96095674, 0.92436597]))]"
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+       0.1639123 , 0.14406443, 0.13234816, 0.1268854 , 0.080721  ]))]"
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+       0.78118932, 0.56369371, 0.56369371, 0.5341562 , 0.44477428]))]"
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+       0.13611729, 0.07929777, 0.05401084, 0.02756743, 0.02121468]))]"
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+       0.61205006, 0.86926402, 0.95475484]), scaled_genome=None, log_likelihood=1740.121929315394, fitness_values=array([0.35654901, 0.24996963, 0.23857508, 0.14754387, 0.10056833,
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+       0.04238977, 0.0341315 , 0.03302382, 0.03250818, 0.03242009]))]"
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+       0.05291186, 0.07135974, 0.035322  , 0.23625565, 0.51668193,
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+       0.06215303, 0.10748968, 0.06630322, 0.23314223, 0.52123645,
+       0.7173524 , 0.91743026, 1.14374436]), scaled_genome=None, log_likelihood=1155.7588448015686, fitness_values=array([0.4979392 , 0.48226255, 0.2893209 , 0.25102149, 0.25102149,
+       0.17146407, 0.16337533, 0.15136816, 0.13113041, 0.13113041]))]"
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+       0.05313104, 0.02814388, 0.019515  , 0.0183652 , 0.01820116]))]"
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+        0.01281413,  0.07666966, -0.13757086,  0.1011165 ,  0.329005  ,
+        0.70621408,  0.79693193,  1.13358685]), scaled_genome=None, log_likelihood=6788.112318806507, fitness_values=array([1.851861  , 1.26333498, 1.08260738, 0.85486324, 0.70190038,
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+       0.73527582, 0.49356193, 0.47065395, 0.44614706, 0.44614706]))]"
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+       0.22553573, 0.02399979, 0.0603341 , 0.02404949, 0.01898419,
+       0.05399179, 0.07882524, 0.073248  , 0.37181636, 0.43144271,
+       0.58085012, 0.68852885, 0.86335485]), scaled_genome=None, log_likelihood=2510.863343183334, fitness_values=array([0.68964234, 0.32026237, 0.14227209, 0.07483176, 0.03996348,
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+       0.06629871, 0.07595141, 0.07329183, 0.36313429, 0.43218173,
+       0.5795437 , 0.69406423, 0.87099448]), scaled_genome=None, log_likelihood=2509.8747129844305, fitness_values=array([0.56699988, 0.27402804, 0.15304253, 0.08451824, 0.04896194,
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+       0.17795377, 0.02395528, 0.05602254, 0.02612805, 0.01656814,
+       0.08065143, 0.06993603, 0.0732723 , 0.36225006, 0.43108814,
+       0.58057209, 0.70807674, 0.88328755]), scaled_genome=None, log_likelihood=2500.921205821279, fitness_values=array([0.7656611 , 0.18993619, 0.14190853, 0.07315568, 0.04765925,
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+       0.57997273, 0.71239516, 0.88444177]), scaled_genome=None, log_likelihood=2509.397375333827, fitness_values=array([0.4170132 , 0.21639759, 0.10733715, 0.07317031, 0.04838911,
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+       0.19077386, 0.02357857, 0.05398511, 0.0276606 , 0.0181439 ,
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+       0.58057022, 0.70448785, 0.87847669]), scaled_genome=None, log_likelihood=2536.5159461065355, fitness_values=array([0.68467856, 0.25132864, 0.1704473 , 0.07436692, 0.06149645,
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+       0.15700897, 0.02409042, 0.05691237, 0.0259489 , 0.01739444,
+       0.0863323 , 0.06671268, 0.07321989, 0.36295824, 0.43112462,
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+       0.04174413, 0.03676919, 0.03377359, 0.02354156, 0.02045596]))]"
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+       0.59973709, 0.67486612, 0.872889  ]), scaled_genome=None, log_likelihood=1675.6540396524383, fitness_values=array([1.18810931, 0.54615513, 0.54615513, 0.48859704, 0.3653452 ,
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+       0.03      , 0.02819615, 0.05426194, 0.03399576, 0.05581928,
+       0.09066342, 0.05263551, 0.08076307, 0.26842409, 0.41883144,
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+       0.34577214, 0.22012469, 0.22012469, 0.22012469, 0.19277137]))]"
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+       0.07353496, 0.05697993, 0.04594156, 0.0409668 , 0.03830201]))]"
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+       0.33465307, 0.33465307, 0.32125148, 0.32125148, 0.32125148]))]"
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+       0.52295232, 0.79479041, 0.99985467]), scaled_genome=None, log_likelihood=1232.6040681083655, fitness_values=array([1.92887265, 1.04505288, 1.00741358, 0.60460558, 0.60460558,
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+        0.11794986,  0.07253724, -0.07463193,  0.06000703,  0.26606689,
+        0.51716009,  0.71223727,  1.0413747 ]), scaled_genome=None, log_likelihood=1991.6472535714215, fitness_values=array([1.30435479, 0.65454847, 0.62172366, 0.56720778, 0.50123294,
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+        0.07071964,  0.01368147, -0.02897315,  0.07257499,  0.28971539,
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+       0.38726557, 0.38726557, 0.38726557, 0.38726557, 0.38726557]))]"
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+       0.01481403, 0.05088918, 0.12264824, 0.19451316, 0.39110175,
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+       0.08942322, 0.04734687, 0.03910583, 0.03547612, 0.03220994]))]"
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+       0.67164575, 0.50975006, 0.50975006, 0.4117283 , 0.4117283 ]))]"
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+       0.32011007, 0.30342636, 0.24894974, 0.20647036, 0.1508257 ]))]"
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+       0.62649462, 0.62649462, 0.6189957 , 0.43011397, 0.43011397]))]"
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+        0.00517931,  0.04102711, -0.05763799,  0.16424189,  0.36081635,
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+        0.00538549,  0.0441027 , -0.01543729,  0.15886776,  0.3397522 ,
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+       0.00420089, 0.03789205, 0.05345157, 0.15009642, 0.33026562,
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+        0.00399466,  0.07653986, -0.08908768,  0.14991807,  0.33782104,
+        0.71107687,  0.81753566,  0.96367109]), scaled_genome=None, log_likelihood=4589.275051574432, fitness_values=array([1.39230307, 1.39230307, 0.87090787, 0.84196528, 0.75487771,
+       0.47029323, 0.47029323, 0.4222141 , 0.4222141 , 0.36780717]))]"
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+        0.12736329,  0.01585515, -0.12867802,  0.16591966,  0.06164932,
+        0.43006182,  0.79980151,  0.9922272 ]), scaled_genome=None, log_likelihood=2621.709710149763, fitness_values=array([2.70684237, 1.66097601, 1.61105736, 0.97050195, 0.90946402,
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+        0.10478281,  0.01575461, -0.12168743,  0.118168  ,  0.26774288,
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+        7.38747496e-01,  9.88146636e-01]), scaled_genome=None, log_likelihood=4662.963828622479, fitness_values=array([3.44420161, 2.08684887, 0.79477455, 0.78368063, 0.77122443,
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+       0.57738541, 0.57738541, 0.56960981, 0.56960981, 0.41252409]))]"
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+       0.1104952 , 0.00934079, 0.04351135, 0.17444833, 0.22673388,
+       0.44202898, 0.7350265 , 0.990693  ]), scaled_genome=None, log_likelihood=3671.930785549669, fitness_values=array([1.47581204, 1.01703382, 0.95957054, 0.60433138, 0.38912658,
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+       0.45068504, 0.73474414, 0.9905249 ]), scaled_genome=None, log_likelihood=3541.63980430089, fitness_values=array([2.82337759, 2.17305066, 0.58781036, 0.4692606 , 0.4058686 ,
+       0.25264597, 0.15996793, 0.09311991, 0.05794652, 0.05093814]))]"
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+       1.51888295, 1.51888295, 1.51888295, 1.51888295, 1.46959438]))]"
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+       2.53614272e-02, 9.95914546e-04, 3.24980276e-02, 4.67390918e-02,
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+       8.57781385e-01, 9.98493740e-01]), scaled_genome=None, log_likelihood=6368.696374161822, fitness_values=array([2.7175152 , 2.7175152 , 2.7175152 , 2.50457089, 2.08742519,
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+       1.12839768, 0.80799716, 0.78099702, 0.61690905, 0.51442185]))]"
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+        0.04033324,  0.03276104,  0.01649941,  0.02625168,  0.00617968,
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+        0.03916129,  0.04146581,  0.00123111,  0.02157017,  0.00639739,
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+       1.66453531, 1.66453531, 1.58279114, 1.58279114, 1.58279114])), Solution(fitness=1.5818131183379391, genome=array([0.14382608, 0.14409682, 0.10288253, 0.07647327, 0.03404485,
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+       0.02968153, 0.03089111, 0.04244342, 0.01080069, 0.03323717,
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+       0.01627283, 0.02341648, 0.02657074, 0.0437449 , 0.0451427 ,
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+       1.93716056, 1.93716056, 1.39756051, 1.39756051, 1.39756051]))]"
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+        8.44071698e-01,  1.00909819e+00]), scaled_genome=None, log_likelihood=5897.199312567553, fitness_values=array([1.95493395, 1.68924742, 1.40481751, 1.25857425, 1.13802051,
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+       0.58689777, 0.74183397, 0.85500807, 0.94552768, 1.10771335]), scaled_genome=None, log_likelihood=3685.5300596098914, fitness_values=array([1.20176851, 0.86769969, 0.78661095, 0.72256522, 0.55758598,
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+       1.1826369 , 0.59420911, 0.3264749 , 0.18142509, 0.07157623]))]"
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+       0.19605796, 0.12964138, 0.08497333, 0.04669767, 0.05861014,
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+       1.80291706, 1.35410248, 1.28866136, 1.18946419, 1.04846658]))]"
+3,synthetic_10_47,GASolver,0.6270130585201107,0.2957670068247987,"[1.05520132 0.85155065 0.7696826  0.4228301  0.39268059 0.79442278
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+       0.77023488, 0.85299091, 0.94917573, 1.00828293, 1.40412061]), scaled_genome=None, log_likelihood=5701.405587673958, fitness_values=array([1.72730377, 1.14052354, 0.74361365, 0.63120744, 0.4643283 ,
+       0.40379311, 0.25568941, 0.15729034, 0.07642949, 0.04304289])), Solution(fitness=0.05762525807952981, genome=array([0.04920495, 0.04365968, 0.17595659, 0.17727871, 0.04896197,
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+       0.1347609 , 0.29546192, 0.08205051, 0.03110411, 0.031724  ,
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+       0.39540644, 0.30902184, 0.25160783, 0.13472745, 0.0650074 ]))]"
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+        0.07903971,  0.00787725,  0.07063435,  0.00533269,  0.01272437,
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+       1.30666075, 1.26773465, 1.12426736, 0.89727405, 0.773722  ]))]"
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+       0.61450179, 0.46600357, 0.459504  , 0.3368452 , 0.299677  ])), Solution(fitness=0.40599394118716275, genome=array([0.17190523, 0.22337593, 0.15867149, 0.11355505, 0.13535532,
+       0.23016275, 0.12507189, 0.03      , 0.03      , 0.08923452,
+       0.05180044, 0.03202167, 0.03718306, 0.01574285, 0.04077894,
+       0.00282379, 0.03314611, 0.02022621, 0.02834597, 0.01080538,
+       0.06382247, 0.17915274, 0.2683454 , 0.3540848 , 0.46064923,
+       0.55884537, 0.6422866 , 0.77701373, 0.87644164, 0.99778025]), scaled_genome=None, log_likelihood=8998.81743268673, fitness_values=array([2.87430822, 2.42802641, 1.47289883, 1.30461633, 0.85956366,
+       0.82621336, 0.65570509, 0.50873492, 0.46939776, 0.40599394])), Solution(fitness=0.3256044483161895, genome=array([0.18463036, 0.19124925, 0.12597579, 0.03426112, 0.05853316,
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+       0.00366769, 0.01985975, 0.03067587, 0.02836527, 0.01058839,
+       0.05881021, 0.18307035, 0.27346125, 0.37505566, 0.46563068,
+       0.55833503, 0.62132336, 0.76338069, 0.8843923 , 0.99236549]), scaled_genome=None, log_likelihood=7789.608341456966, fitness_values=array([3.10144086, 1.45202313, 0.90726875, 0.90726875, 0.47680811,
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+       0.29153066, 0.07324758, 0.05164584, 0.04007066, 0.09570695,
+       0.03850441, 0.03790157, 0.03425916, 0.03969971, 0.04489223,
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+       0.59252595, 0.53806475, 0.39890936, 0.39462868, 0.35315165])), Solution(fitness=0.32648251299728587, genome=array([0.23746696, 0.14424062, 0.13575364, 0.03      , 0.0851179 ,
+       0.28872192, 0.11204733, 0.03      , 0.03      , 0.06796186,
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+       0.58159498, 0.56471073, 0.53181662, 0.37271383, 0.3244608 ])), Solution(fitness=0.3501059332929611, genome=array([0.10528582, 0.18840044, 0.14756672, 0.14474958, 0.06523198,
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+       5.63810316, 4.29743199, 3.70550209, 2.44030022, 2.02355713]))]"
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+       1.67239052, 1.36111027, 1.35297389, 1.23566168, 1.14068671]))]"
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+        2.44523723,  2.09795158,  1.12012817,  0.53117332,  0.36593   ])), Solution(fitness=0.4511804172580158, genome=array([0.13900696, 0.03029006, 0.03568983, 0.29375208, 0.24569372,
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+       -0.00279542,  0.15856918,  0.25534169,  0.37907402,  0.4065096 ,
+        0.51263107,  0.60480689,  0.72338034,  0.82916649,  0.9908546 ]), scaled_genome=None, log_likelihood=14651.194971326218, fitness_values=array([8.13911009, 8.13911009, 6.92803359, 6.92803359, 3.13133909,
+       2.87024285, 1.96741198, 1.47393985, 0.76376201, 0.40142835])), Solution(fitness=0.36588872576503645, genome=array([0.180715  , 0.03      , 0.03673824, 0.21521464, 0.19886978,
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+        1.90020691,  1.5975531 ,  1.09633034,  0.6124335 ,  0.28322733]))]"
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+       0.44025864, 0.6114219 , 0.68359132, 0.8430504 , 0.9493453 ]), scaled_genome=None, log_likelihood=3492.7125079022476, fitness_values=array([3.08748255, 1.53244394, 1.53244394, 1.53244394, 1.30131397,
+       1.30131397, 1.22130145, 1.22130145, 1.10679644, 1.07560812])), Solution(fitness=0.8521871560182274, genome=array([ 0.08677871,  0.39493291,  0.03      ,  0.11407262,  0.13905823,
+        0.08050368,  0.12798597,  0.03970836,  0.19267265,  0.37626208,
+        0.02687094,  0.01915698,  0.03238403,  0.01288951,  0.03249856,
+        0.02477244,  0.01773497,  0.02544735,  0.0478811 ,  0.02041169,
+       -0.00298027,  0.06647379,  0.17052944,  0.2434651 ,  0.34781899,
+        0.49110723,  0.59550795,  0.6547947 ,  0.84614996,  0.93600747]), scaled_genome=None, log_likelihood=7777.762708864562, fitness_values=array([2.73787992, 2.73787992, 2.69184879, 1.96630676, 1.01416822,
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+       0.01479427, 0.01664775, 0.05347488, 0.03156839, 0.01967537,
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+       0.00782584, 0.07943395, 0.13302204, 0.28478586, 0.37594464,
+       0.47747956, 0.64586985, 0.73067347, 0.78615116, 0.9211539 ]), scaled_genome=None, log_likelihood=3244.398698528592, fitness_values=array([2.49692826, 1.83000727, 1.00571828, 1.00571828, 1.00571828,
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+        0.03765777,  0.04405077,  0.01144789,  0.03106796,  0.01587138,
+        0.02704459,  0.03413218,  0.01300368,  0.02674153,  0.032186  ,
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+        0.46245842,  0.6301887 ,  0.72163889,  0.80481672,  0.92848267]), scaled_genome=None, log_likelihood=2865.3852062635688, fitness_values=array([2.36189475, 1.30840066, 1.30840066, 1.30840066, 1.30840066,
+       1.0792208 , 0.95186066, 0.95186066, 0.85857802, 0.85857802])), Solution(fitness=1.0231389140333413, genome=array([ 0.10957531,  0.17960393,  0.21932187,  0.03      ,  0.07397793,
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+        0.02785091,  0.01289856,  0.01383101,  0.03080096,  0.03374494,
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+       1.02313891, 1.02313891, 1.02313891, 1.02313891, 1.02313891]))]"
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+       0.5895901 , 0.41918872, 0.26221116, 0.16402482, 0.0871219 ]))]"
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+       2.25151653, 2.25151653, 2.25151653, 2.23745239, 2.04622339]))]"
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+       1.38924783, 1.1497373 , 1.00895731, 0.91364121, 0.85498081])), Solution(fitness=0.7939685546534553, genome=array([0.24112974, 0.14022738, 0.05104023, 0.06840687, 0.17323664,
+       0.19012336, 0.03      , 0.06741507, 0.1355508 , 0.32453302,
+       0.04287283, 0.0341066 , 0.02660177, 0.01453912, 0.03141901,
+       0.01799827, 0.02817361, 0.01743397, 0.02835336, 0.01409686,
+       0.0223013 , 0.13122924, 0.2721007 , 0.32117597, 0.45762053,
+       0.49432486, 0.59432466, 0.66175316, 0.73113119, 0.88682716]), scaled_genome=None, log_likelihood=9850.387329855148, fitness_values=array([4.02814307, 2.98359031, 2.71728625, 1.99529541, 1.651164  ,
+       1.30232078, 1.10766534, 0.90506185, 0.87155901, 0.79396855])), Solution(fitness=0.8398768460055077, genome=array([0.26671795, 0.06750339, 0.03      , 0.04433604, 0.14609337,
+       0.20551206, 0.05403136, 0.11304238, 0.0792601 , 0.3073136 ,
+       0.05278673, 0.03652203, 0.01578585, 0.02376748, 0.02869118,
+       0.02171194, 0.01035171, 0.0178447 , 0.03969965, 0.01551722,
+       0.03453153, 0.16380997, 0.26287667, 0.32400146, 0.4376719 ,
+       0.48568308, 0.63742398, 0.68604608, 0.76491804, 0.88801749]), scaled_genome=None, log_likelihood=8930.269728117908, fitness_values=array([2.98727372, 2.98727372, 2.23858722, 1.78819827, 1.61961476,
+       1.21208375, 1.15206544, 0.94925203, 0.84673835, 0.83987685])), Solution(fitness=0.7889795323255905, genome=array([0.22384913, 0.13549971, 0.03      , 0.07953767, 0.16091886,
+       0.19493415, 0.04141304, 0.08180848, 0.05287874, 0.33257766,
+       0.05158798, 0.04823547, 0.02079706, 0.02426791, 0.02612501,
+       0.01667408, 0.01759724, 0.02492686, 0.02732072, 0.01521368,
+       0.0380205 , 0.14680851, 0.26815992, 0.35711155, 0.45157174,
+       0.49047415, 0.60035567, 0.68857498, 0.76769945, 0.88544292]), scaled_genome=None, log_likelihood=9625.02245006635, fitness_values=array([4.24319424, 3.31109148, 2.09599456, 1.56937182, 1.3766978 ,
+       1.17123005, 0.96866752, 0.95286547, 0.78897953, 0.78897953])), Solution(fitness=0.9181505704220683, genome=array([0.18331275, 0.12353561, 0.03652923, 0.03      , 0.10290703,
+       0.23213763, 0.03614545, 0.09458467, 0.05192343, 0.32231253,
+       0.045334  , 0.02624691, 0.02762057, 0.01419615, 0.02270363,
+       0.02099348, 0.02124953, 0.02296287, 0.02747244, 0.01683686,
+       0.04644424, 0.14822499, 0.24084206, 0.32143116, 0.431616  ,
+       0.48377861, 0.58049124, 0.67458255, 0.79041225, 0.88760325]), scaled_genome=None, log_likelihood=8558.254551533078, fitness_values=array([4.5654374 , 4.09101382, 2.88067099, 2.21813126, 1.73950476,
+       1.48046483, 1.18758335, 0.95732824, 0.95405326, 0.91815057])), Solution(fitness=0.791813985222729, genome=array([0.24489749, 0.08673566, 0.06013765, 0.06484124, 0.1649402 ,
+       0.19324545, 0.03      , 0.09433377, 0.04179764, 0.36160974,
+       0.04558118, 0.02241835, 0.0295096 , 0.01168411, 0.02582517,
+       0.01612137, 0.03663133, 0.0155612 , 0.02723955, 0.01494742,
+       0.04668055, 0.17411958, 0.2565888 , 0.34145541, 0.45414689,
+       0.49005667, 0.61985492, 0.6893222 , 0.79804219, 0.88584913]), scaled_genome=None, log_likelihood=9761.115596523123, fitness_values=array([6.24787475, 3.07836813, 2.72525833, 1.59996946, 1.34309791,
+       1.13416065, 1.05033905, 0.9248458 , 0.897293  , 0.79181399])), Solution(fitness=0.7875710944246731, genome=array([0.30519315, 0.0714554 , 0.06500565, 0.07223667, 0.12162859,
+       0.23638642, 0.06040075, 0.08894384, 0.06150967, 0.36407247,
+       0.06096776, 0.02107457, 0.02029041, 0.01399268, 0.02770194,
+       0.01778364, 0.00754313, 0.01514129, 0.03305505, 0.01491016,
+       0.02903328, 0.19148531, 0.27202695, 0.33487386, 0.44543007,
+       0.49106674, 0.60492548, 0.67330253, 0.76346013, 0.88375666]), scaled_genome=None, log_likelihood=9928.18401023853, fitness_values=array([3.42064014, 2.13170651, 2.13170651, 1.58242976, 1.30909609,
+       1.00421713, 0.98930223, 0.94793311, 0.91455612, 0.78757109])), Solution(fitness=0.7620526647937655, genome=array([1.65254432e-01, 1.84071567e-01, 4.08090098e-02, 5.12442954e-02,
+       9.22728182e-02, 2.03733443e-01, 5.55797038e-02, 1.05607499e-01,
+       4.10038415e-02, 2.65625174e-01, 2.01692033e-02, 3.80505521e-02,
+       1.54894148e-02, 1.47914066e-02, 2.59864966e-02, 2.06571716e-02,
+       1.42915013e-02, 1.30172104e-02, 2.56912185e-02, 1.51251244e-02,
+       7.96001595e-04, 1.35247898e-01, 2.66422909e-01, 3.49032753e-01,
+       4.43865469e-01, 4.93142162e-01, 6.07976087e-01, 6.73126569e-01,
+       7.68572638e-01, 8.87365575e-01]), scaled_genome=None, log_likelihood=8667.975370695094, fitness_values=array([4.92930267, 3.05390106, 2.49883011, 2.04792098, 1.64087642,
+       1.22109808, 1.12618324, 0.95157199, 0.82915193, 0.76205266])), Solution(fitness=0.7512182790264045, genome=array([ 0.16528662,  0.23604849,  0.1109627 ,  0.04003302,  0.10490744,
+        0.2102434 ,  0.03      ,  0.11896776,  0.04669947,  0.46036852,
+        0.03349947,  0.05414464,  0.02219657,  0.01444032,  0.02260451,
+        0.02047644,  0.02654809,  0.01378043,  0.0343059 ,  0.01759148,
+       -0.00631672,  0.13246681,  0.25387291,  0.33722343,  0.43234518,
+        0.48446402,  0.62354878,  0.68312442,  0.78848287,  0.88846415]), scaled_genome=None, log_likelihood=10763.73699412794, fitness_values=array([5.84589165, 2.6052162 , 2.29229895, 1.72308432, 1.62225738,
+       1.14117005, 1.07902872, 0.98710904, 0.83977893, 0.75121828]))]"
+3,synthetic_10_78,GASolver,0.55892683852409,0.18576440401575692,"[0.65587985 0.30057748 0.59608341 0.91050323 0.23294417 0.56954227
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+       0.47896136, 0.60386625, 0.675826  , 0.82595067, 0.88314148]), scaled_genome=None, log_likelihood=7752.575150420629, fitness_values=array([1.74967339, 1.32886969, 1.17542001, 0.95281359, 0.82761984,
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+       0.47857909, 0.5676318 , 0.67394965, 0.8800092 , 0.90350496]), scaled_genome=None, log_likelihood=7843.086122251983, fitness_values=array([1.83296634, 1.47190321, 1.20137323, 1.06881447, 0.84307897,
+       0.76831686, 0.68411257, 0.6716617 , 0.59656806, 0.59608341])), Solution(fitness=0.910503233856429, genome=array([0.22539773, 0.05433468, 0.03373607, 0.05140951, 0.19854052,
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+       0.01804207, 0.07456548, 0.01385606, 0.04720528, 0.01679248,
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+       0.55994801, 0.51735917, 0.50766882, 0.4771394 , 0.46182719])), Solution(fitness=0.6968187945283542, genome=array([0.19734047, 0.07364262, 0.04152076, 0.04100875, 0.09249058,
+       0.15522646, 0.05207527, 0.03604479, 0.274247  , 0.0364033 ,
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+       0.02203824, 0.01715304, 0.05440885, 0.00661115, 0.06246008,
+       0.04408646, 0.13336522, 0.24361013, 0.3283835 , 0.43261406,
+       0.49273917, 0.67305325, 0.74893033, 0.87901142, 0.93322901]), scaled_genome=None, log_likelihood=7388.001032659538, fitness_values=array([2.14356184, 1.51431546, 1.28299927, 1.08343532, 1.06306335,
+       0.97400583, 0.90005758, 0.89004008, 0.77578383, 0.69681879]))]"
+4,synthetic_10_78,LSHADESolver,0.4519788164795207,0.08167139518842273,"[0.56019601 0.44343054 0.5088271  0.3723026  0.41525407 0.43296971
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+       1.52462715, 1.28543274, 0.89072485, 0.70470857, 0.56019601])), Solution(fitness=0.4434305438938773, genome=array([ 0.21656307,  0.14079355,  0.09533097,  0.04562168,  0.07209018,
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+        0.01848501,  0.0282942 ,  0.03858119,  0.04497185,  0.018092  ,
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+        0.48497495,  0.62792779,  0.67901231,  0.74625375,  0.88525473]), scaled_genome=None, log_likelihood=10955.931230996659, fitness_values=array([6.64842226, 4.55904785, 2.70302835, 1.90271996, 1.84521223,
+       1.49095512, 1.03578107, 0.8866547 , 0.66277664, 0.44343054])), Solution(fitness=0.5088270981667117, genome=array([2.75356390e-01, 1.73488824e-01, 4.19497549e-02, 3.15892181e-02,
+       1.10299543e-01, 2.20786256e-01, 3.35662682e-02, 7.67085986e-02,
+       3.50388583e-02, 4.08879014e-01, 3.48959191e-02, 3.57943913e-02,
+       4.14739559e-02, 2.50330574e-02, 1.97127905e-02, 1.49442268e-02,
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+       7.55107815e-01, 8.88215607e-01]), scaled_genome=None, log_likelihood=10516.533194956337, fitness_values=array([6.5446652 , 4.75218209, 2.80152472, 1.8596408 , 1.58529185,
+       1.18206712, 1.01793639, 0.73231591, 0.6371893 , 0.5088271 ])), Solution(fitness=0.372302596901702, genome=array([9.65637546e-02, 1.63373935e-01, 4.68942921e-02, 4.84696176e-02,
+       1.40958538e-01, 1.71849962e-01, 3.18496683e-02, 7.85582185e-02,
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+       7.41195002e-01, 8.87800051e-01]), scaled_genome=None, log_likelihood=10979.632301452006, fitness_values=array([6.12683572, 4.68156418, 2.91523835, 2.1825236 , 1.66299427,
+       1.57179793, 1.05572488, 0.86562932, 0.53828047, 0.3723026 ])), Solution(fitness=0.415254069647964, genome=array([ 0.20036259,  0.12323845,  0.11366661,  0.03987286,  0.12271561,
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+        0.48813649,  0.55630458,  0.67566312,  0.74838159,  0.8859934 ]), scaled_genome=None, log_likelihood=11274.711206157677, fitness_values=array([6.30035107, 4.04883444, 2.21510786, 2.21510786, 1.98707585,
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+       0.11962228, 0.20787396, 0.30150201, 0.36264833, 0.51827448,
+       0.5942527 , 0.70883064, 0.85409186, 0.91069103, 0.96239003]), scaled_genome=None, log_likelihood=7773.563433373573, fitness_values=array([4.7591845 , 2.09294146, 2.09294146, 2.09294146, 2.03525942,
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+       3.29637146, 2.050283  , 2.050283  , 2.050283  , 2.050283  ])), Solution(fitness=1.9140298813205356, genome=array([0.24567375, 0.0791098 , 0.06198331, 0.20169715, 0.23618762,
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+       0.58738494, 0.69371377, 0.86273292, 0.9106479 , 0.99523202]), scaled_genome=None, log_likelihood=3321.970463189293, fitness_values=array([5.14854274, 3.64693655, 2.95911589, 2.36585071, 2.27433515,
+       1.94402399, 1.94402399, 1.94402399, 1.62529409, 1.62529409])), Solution(fitness=1.4195758751860104, genome=array([0.12473219, 0.17797302, 0.03      , 0.03      , 0.15338224,
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+       0.59906203, 0.68318563, 0.77204088, 0.89253508, 1.00673145]), scaled_genome=None, log_likelihood=3729.3138314645203, fitness_values=array([3.32204754, 3.32204754, 2.87498194, 1.95871672, 1.51755499,
+       1.51755499, 1.51755499, 1.51755499, 1.41957588, 1.41957588])), Solution(fitness=1.4237366095006545, genome=array([0.09010249, 0.15596926, 0.03      , 0.08207933, 0.15862128,
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+       0.06015993, 0.0248912 , 0.01607384, 0.01556196, 0.0465311 ,
+       0.0340261 , 0.02241762, 0.02624068, 0.0259146 , 0.02103163,
+       0.11084406, 0.19317867, 0.32072107, 0.40087198, 0.55594744,
+       0.62388343, 0.73675137, 0.82437479, 0.9028002 , 0.99881272]), scaled_genome=None, log_likelihood=3164.5709737802363, fitness_values=array([3.24874269, 2.9893848 , 2.0088377 , 1.42373661, 1.42373661,
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+       0.12666113, 0.20643769, 0.07982228, 0.09687249, 0.23139598,
+       0.04732966, 0.01941799, 0.01514196, 0.00429519, 0.0255092 ,
+       0.02951991, 0.03888401, 0.03387674, 0.03816816, 0.03036154,
+       0.10537491, 0.18086529, 0.27081571, 0.40209778, 0.48148908,
+       0.57892608, 0.70991686, 0.80394001, 0.90709962, 0.99802847]), scaled_genome=None, log_likelihood=3203.1844361897456, fitness_values=array([3.15988243, 2.65214698, 2.65214698, 2.65214698, 2.19278941,
+       2.19278941, 1.64321594, 1.64321594, 1.64321594, 1.64321594])), Solution(fitness=1.8271089046731175, genome=array([0.14992018, 0.08874877, 0.03      , 0.13299536, 0.11540041,
+       0.15299616, 0.13869278, 0.24281494, 0.15444587, 0.17053291,
+       0.03004823, 0.01525489, 0.03077315, 0.02366561, 0.02035113,
+       0.01707707, 0.02854657, 0.01822487, 0.06773192, 0.00532958,
+       0.08519501, 0.21416835, 0.29266572, 0.39636091, 0.50377412,
+       0.56673092, 0.664331  , 0.77238291, 0.90469406, 0.99276376]), scaled_genome=None, log_likelihood=2643.841111251088, fitness_values=array([3.0461658 , 3.0461658 , 3.0461658 , 3.04427011, 2.65944802,
+       2.4035931 , 2.05253462, 1.8271089 , 1.8271089 , 1.8271089 ])), Solution(fitness=1.4638369150930284, genome=array([0.1452239 , 0.17931523, 0.05318697, 0.03      , 0.14217048,
+       0.07272351, 0.03      , 0.03      , 0.22932947, 0.24827288,
+       0.02220258, 0.03969971, 0.0110741 , 0.04374248, 0.00488453,
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+       0.57044862, 0.65324281, 0.73947128, 0.92268292, 1.01520794]), scaled_genome=None, log_likelihood=2628.8365856271853, fitness_values=array([2.8465742 , 2.8465742 , 1.91115695, 1.91115695, 1.91115695,
+       1.78104206, 1.75941557, 1.57698186, 1.46383692, 1.46383692])), Solution(fitness=1.0542259532490506, genome=array([0.15324423, 0.05654434, 0.15946556, 0.03      , 0.11861787,
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+       0.01751097, 0.0172353 , 0.01388812, 0.03453389, 0.01772433,
+       0.0327776 , 0.17944678, 0.27094095, 0.36277755, 0.51603324,
+       0.58940937, 0.70706555, 0.76727556, 0.89122008, 1.00418843]), scaled_genome=None, log_likelihood=4951.680742733213, fitness_values=array([3.34278678, 3.34278678, 3.29156677, 2.21434725, 2.07694788,
+       1.70992091, 1.4992994 , 1.23728222, 1.05422595, 1.05422595])), Solution(fitness=1.3781174772874756, genome=array([0.05225076, 0.25507661, 0.07155346, 0.10555353, 0.20827042,
+       0.03      , 0.11455743, 0.0911971 , 0.15051278, 0.21457031,
+       0.04364925, 0.03145452, 0.01235167, 0.07254111, 0.02480736,
+       0.02163579, 0.04470351, 0.004801  , 0.05751839, 0.01107431,
+       0.04920482, 0.1791692 , 0.27073656, 0.42017415, 0.50610156,
+       0.60184634, 0.72036813, 0.76008228, 0.9374641 , 0.99892348]), scaled_genome=None, log_likelihood=3805.963065259317, fitness_values=array([3.95322639, 3.13820504, 3.13820504, 2.06804167, 2.06804167,
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+       1.4065178 , 1.4065178 , 1.4065178 , 1.08739894, 1.02994071])), Solution(fitness=1.337380809020141, genome=array([0.14803216, 0.1766869 , 0.09988774, 0.1082771 , 0.06529435,
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+       1.65985703, 1.42474537, 1.42474537, 1.37333421, 1.33738081]))]"
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+       0.8342481 , 0.75334911, 0.68186829, 0.63240776, 0.56572615]))]"
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+       0.02759095, 0.03239341, 0.03113575, 0.03726845, 0.04015249,
+       0.02103428, 0.10170628, 0.18755812, 0.25887473, 0.41214806,
+       0.52013174, 0.64392947, 0.77401603, 0.91052261, 1.0097714 ]), scaled_genome=None, log_likelihood=3975.3427700246984, fitness_values=array([2.53218825, 1.48107993, 1.33469293, 1.26666118, 1.05789742,
+       0.82221245, 0.75163456, 0.64006319, 0.55639598, 0.52523023])), Solution(fitness=0.5929538493717708, genome=array([0.10807734, 0.22868277, 0.2636549 , 0.12271337, 0.0963846 ,
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+       0.8871629 , 0.73427617, 0.60021784, 0.58946896, 0.54944666])), Solution(fitness=0.5316441553373091, genome=array([0.16335851, 0.18237928, 0.21678406, 0.22813851, 0.09589472,
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+       0.84989625, 0.79696767, 0.68300782, 0.42677338, 0.23794488]))]"
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+       1.44589819, 1.44589819, 1.30087189, 1.12772065, 1.0420571 ]))]"
+2,synthetic_10_40,SHADESolver,0.3114087243364914,0.026425419841631496,"[0.33195906 0.31128931 0.31502181 0.25362138 0.35307536 0.31253939
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+       0.32399164, 0.316565  , 0.2540059 , 0.22641239, 0.20972919])), Solution(fitness=0.2448289490931006, genome=array([ 0.03712371,  0.16502743,  0.05134423,  0.0445705 ,  0.03302282,
+        0.03834367,  0.0951216 ,  0.13432826,  0.23923852,  0.16187927,
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+       0.40010682, 0.37715983, 0.28246715, 0.255644  , 0.24482895])), Solution(fitness=0.1552665695407899, genome=array([ 2.54634375e-02,  1.71938605e-01,  6.54971647e-02,  3.19858910e-02,
+        3.46619432e-02,  1.05480431e-01,  1.35293725e-01,  1.67081372e-01,
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+        0.04379838,  0.05259165,  0.01666544,  0.02216395,  0.0739287 ,
+        0.02431956,  0.03421371,  0.04766269,  0.05618947,  0.05874857,
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+        0.0695065 ,  0.03039467,  0.03398881,  0.0286253 ,  0.03951229,
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+       0.03145467, 0.02733496, 0.01183803, 0.03806035, 0.00480107,
+       0.0063724 , 0.04277294, 0.02284895, 0.01929591, 0.02205237,
+       0.05136518, 0.11632929, 0.24654465, 0.43069758, 0.50740802,
+       0.5476511 , 0.70878823, 0.7671115 , 0.93026249, 1.01477598]), scaled_genome=None, log_likelihood=6811.168729646051, fitness_values=array([2.02494626, 1.1363178 , 0.92944417, 0.77679506, 0.59825403,
+       0.46359332, 0.33400542, 0.19854307, 0.1004054 , 0.04241503])), Solution(fitness=0.1343248956524995, genome=array([0.14506989, 0.18126564, 0.16404261, 0.06149711, 0.16733685,
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+       0.02441944, 0.03583992, 0.01155336, 0.02906916, 0.00464222,
+       0.00638017, 0.03803461, 0.02342228, 0.01956797, 0.02462905,
+       0.04470791, 0.11141185, 0.24578088, 0.4262388 , 0.50710662,
+       0.54757821, 0.74244895, 0.8594873 , 0.93188587, 1.02296116]), scaled_genome=None, log_likelihood=7827.976234565178, fitness_values=array([1.9580059 , 1.32362575, 1.06375879, 0.75496157, 0.64578082,
+       0.48687146, 0.40196386, 0.33260993, 0.23677481, 0.1343249 ])), Solution(fitness=0.06385618123278308, genome=array([0.12139628, 0.14950658, 0.14762916, 0.04008134, 0.14175418,
+       0.07863261, 0.20950258, 0.03895099, 0.10921392, 0.0342596 ,
+       0.0299068 , 0.0252075 , 0.01065809, 0.05250092, 0.00451167,
+       0.00642257, 0.03961487, 0.01772583, 0.01685752, 0.04167916,
+       0.04541978, 0.11583276, 0.24630694, 0.41533443, 0.50745376,
+       0.54760898, 0.7459609 , 0.90390876, 0.93547469, 0.99670419]), scaled_genome=None, log_likelihood=6835.152881922653, fitness_values=array([1.5080773 , 1.06745775, 1.0158437 , 0.71909177, 0.49144804,
+       0.38044742, 0.283702  , 0.19782856, 0.11511866, 0.06385618])), Solution(fitness=0.238990273747196, genome=array([0.16813558, 0.11034349, 0.15022928, 0.03119311, 0.29710038,
+       0.05184385, 0.09784406, 0.03715991, 0.14195871, 0.03035787,
+       0.0340476 , 0.02139194, 0.01227267, 0.02842563, 0.02002383,
+       0.03780077, 0.0147684 , 0.03981756, 0.01955755, 0.03592898,
+       0.05594548, 0.12299959, 0.24707974, 0.41227515, 0.51849569,
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+       0.47790857, 0.353822  , 0.29355012, 0.25764634, 0.23899027])), Solution(fitness=0.2380982556300239, genome=array([0.11927833, 0.15136359, 0.15492053, 0.04065276, 0.29301865,
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+       0.04275062, 0.0149763 , 0.04112503, 0.01873683, 0.04360751,
+       0.04587339, 0.11311263, 0.2456187 , 0.43573368, 0.51869802,
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+       0.02539831, 0.02123673, 0.02271646, 0.01542799, 0.06781887,
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+       0.6790895 , 0.75575015, 0.89524073, 0.93240067, 0.99398459]), scaled_genome=None, log_likelihood=6333.4834066033945, fitness_values=array([2.16228344, 1.41155696, 0.91186921, 0.7964045 , 0.54976561,
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+       0.04025674, 0.11526796, 0.24615463, 0.4194209 , 0.5066653 ,
+       0.54801882, 0.74511216, 0.89590943, 0.93185784, 1.00973397]), scaled_genome=None, log_likelihood=6507.330967732648, fitness_values=array([1.99610932, 1.47708   , 0.99527385, 0.82804072, 0.59709574,
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+       0.03141429, 0.02612929, 0.01131988, 0.03300584, 0.021142  ,
+       0.02660225, 0.01808741, 0.03402674, 0.01970528, 0.04609   ,
+       0.04852998, 0.11650951, 0.24709787, 0.41408483, 0.51841011,
+       0.69000264, 0.75378572, 0.80058715, 0.92952791, 1.00224262]), scaled_genome=None, log_likelihood=6228.4528687129205, fitness_values=array([1.58469347, 1.07802305, 0.8059721 , 0.61599811, 0.4590838 ,
+       0.40796803, 0.35868787, 0.30287598, 0.25872265, 0.23821283])), Solution(fitness=0.23402289517375555, genome=array([0.14287667, 0.12481012, 0.14921757, 0.03150785, 0.30016453,
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+       0.05153486, 0.11966401, 0.24651863, 0.41218717, 0.51892264,
+       0.6878396 , 0.75443544, 0.78856747, 0.92888332, 1.00436266]), scaled_genome=None, log_likelihood=6040.662306031085, fitness_values=array([1.91046349, 1.27822557, 0.96724094, 0.68631237, 0.54146803,
+       0.43562458, 0.34460657, 0.29197155, 0.25279845, 0.2340229 ]))]"
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+       0.03775736, 0.00796863, 0.0108507 , 0.02479038, 0.03566834,
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+       1.9211073 , 1.28953255, 1.28953255, 1.2641169 , 1.2641169 ])), Solution(fitness=1.066901670256086, genome=array([0.1182554 , 0.15434926, 0.18291496, 0.03      , 0.25443432,
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+       0.03256469, 0.00985075, 0.01675483, 0.0327368 , 0.01214256,
+       0.03945699, 0.11753825, 0.25289755, 0.37638448, 0.52043543,
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+       1.47347446, 1.36636015, 1.36636015, 1.06690167, 1.06690167]))]"
+2,synthetic_10_41,SHADESolver,0.42592688182382343,0.01605577822730906,"[0.42625651 0.42309352 0.41542111 0.44617573 0.40918421 0.44415275
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+       0.06975714, 0.12495709, 0.24610565, 0.41048812, 0.52293521,
+       0.68023589, 0.75763293, 0.88257384, 0.93183389, 1.02543236]), scaled_genome=None, log_likelihood=5638.487778690393, fitness_values=array([2.07114276, 1.52230159, 1.0065354 , 0.83693976, 0.64893427,
+       0.64893427, 0.59856934, 0.54326725, 0.48120555, 0.44415275])), Solution(fitness=0.4404752347258236, genome=array([0.10660167, 0.1290478 , 0.13752825, 0.06655162, 0.23043254,
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+       0.03330765, 0.0274997 , 0.03310193, 0.02072345, 0.03240875,
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+       0.8893847 , 0.55500078, 0.26557413, 0.16778784, 0.07340754]))]"
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+       0.52627103, 0.62975431, 0.73633962, 0.82487026, 0.8962995 ]), scaled_genome=None, log_likelihood=6871.923488880968, fitness_values=array([4.43487684, 2.59692847, 1.00218173, 0.93645786, 0.8385873 ,
+       0.63958736, 0.62868631, 0.45881918, 0.43639578, 0.40011287])), Solution(fitness=0.43792495153024735, genome=array([0.15081854, 0.13432545, 0.19460321, 0.08658302, 0.10606094,
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+        0.01843079,  0.03050511,  0.01636883,  0.02141362,  0.04088887,
+        0.01824585,  0.02966348,  0.01828665,  0.0327014 ,  0.03268039,
+       -0.00477142,  0.1881549 ,  0.24045046,  0.30677414,  0.46935915,
+        0.52316721,  0.62985794,  0.71501665,  0.83162673,  0.8931247 ]), scaled_genome=None, log_likelihood=8362.503796251403, fitness_values=array([4.63344813, 2.39406506, 1.54967858, 1.04506244, 0.95932781,
+       0.83779731, 0.70424946, 0.63979502, 0.54161697, 0.43924377])), Solution(fitness=0.4277984621662196, genome=array([ 0.24081182,  0.1263412 ,  0.17885648,  0.11821352,  0.13518482,
+        0.21096698,  0.10986829,  0.06029083,  0.1218168 ,  0.16001516,
+        0.02354536,  0.02299295,  0.00975782,  0.02826071,  0.03000205,
+        0.01443539,  0.02576593,  0.03760312,  0.02479974,  0.03077323,
+       -0.00415906,  0.1780957 ,  0.24181851,  0.30035742,  0.45906663,
+        0.53308256,  0.63552903,  0.72472127,  0.83451592,  0.90069029]), scaled_genome=None, log_likelihood=7913.85228589836, fitness_values=array([2.50171968, 1.96940856, 1.35468514, 1.00308721, 0.73038888,
+       0.72273608, 0.54381914, 0.48785207, 0.44774048, 0.42779846])), Solution(fitness=0.4358771088826875, genome=array([0.15266579, 0.16829869, 0.15375291, 0.10223006, 0.17796734,
+       0.15932859, 0.14430264, 0.08115086, 0.07072852, 0.16931277,
+       0.01795429, 0.02511288, 0.01530715, 0.02234832, 0.03367607,
+       0.0144492 , 0.03986525, 0.01884789, 0.03240154, 0.02507475,
+       0.00251304, 0.19521807, 0.24493223, 0.30484126, 0.46432053,
+       0.52381141, 0.62893137, 0.74449242, 0.81267258, 0.90125856]), scaled_genome=None, log_likelihood=7523.689929799329, fitness_values=array([3.83092992, 2.29190223, 1.47698592, 1.16489501, 0.95426998,
+       0.80424956, 0.59336876, 0.52021754, 0.49471655, 0.43587711])), Solution(fitness=0.43101601020205876, genome=array([1.18411291e-01, 1.39324136e-01, 1.84122746e-01, 1.28317964e-01,
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+       1.02879661e-01, 1.02891121e-01, 1.98885641e-02, 2.64872418e-02,
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+       2.66364155e-02, 3.68087773e-02, 2.86763796e-02, 3.25518457e-02,
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+       4.49983151e-01, 5.24790446e-01, 6.39964683e-01, 7.60616040e-01,
+       8.48768320e-01, 9.18541103e-01]), scaled_genome=None, log_likelihood=6744.031598680219, fitness_values=array([2.04882714, 1.99318543, 1.6890127 , 1.26537275, 0.98790588,
+       0.68975473, 0.64224551, 0.59431284, 0.54952601, 0.43101601])), Solution(fitness=0.4982658166525903, genome=array([0.17057328, 0.1274381 , 0.24165914, 0.11759359, 0.16319877,
+       0.2558435 , 0.13055456, 0.05255884, 0.05835983, 0.1547947 ,
+       0.02069916, 0.02352373, 0.02728575, 0.02296206, 0.02567668,
+       0.02160151, 0.03435242, 0.01611177, 0.02346918, 0.02516519,
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+       0.0206555 , 0.01484049, 0.02766886, 0.02574629, 0.02283346,
+       0.01831073, 0.02999676, 0.01869318, 0.04171251, 0.0298503 ,
+       0.00177608, 0.1766903 , 0.25455072, 0.33185228, 0.45447068,
+       0.52982105, 0.62841186, 0.73940094, 0.84025205, 0.91297752]), scaled_genome=None, log_likelihood=6481.799241714061, fitness_values=array([4.11818093, 1.4429936 , 1.30655015, 1.1053878 , 0.94258674,
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+       0.52299661, 0.63366594, 0.7842169 , 0.818138  , 0.89861891]), scaled_genome=None, log_likelihood=7894.658567422728, fitness_values=array([2.92559583, 2.91634006, 1.93750585, 1.35513457, 1.19360037,
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+       -0.00215845,  0.1859827 ,  0.24075208,  0.30490783,  0.44674339,
+        0.52272214,  0.62814462,  0.73363548,  0.81116782,  0.89309372]), scaled_genome=None, log_likelihood=7428.929629682065, fitness_values=array([3.51590612, 2.44804227, 1.93709946, 1.73971577, 0.90918914,
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+        0.0179867 ,  0.02577544,  0.0324474 ,  0.00882854,  0.02774484,
+       -0.00140986,  0.19006261,  0.2400959 ,  0.3034755 ,  0.44280526,
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+       8.87616161e-01, 9.42694100e-01]), scaled_genome=None, log_likelihood=7449.984461074195, fitness_values=array([4.21404737, 3.12195665, 2.09516673, 1.56081694, 1.18208632,
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+       0.679905  , 0.59410312, 0.40940945, 0.21049005, 0.11721685]))]"
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+       2.10287172, 1.94755767, 1.94755767, 1.94755767, 1.94755767]))]"
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+       1.00740353, 0.99227185, 0.97966581, 0.93140878, 0.91262586]))]"
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+        0.05939139,  0.05923198,  0.05561447,  0.05560424,  0.02881112,
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+       0.61808022, 0.61363146, 0.59040245, 0.57442587, 0.52891835]))]"
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+       2.73783356e-02, 6.90992431e-02, 1.83596599e-02, 5.49504316e-03,
+       7.27053126e-02, 1.76911942e-02, 6.32917023e-02, 4.49038951e-02,
+       2.38356767e-04, 1.73791296e-01, 2.30596450e-01, 3.30280416e-01,
+       5.25626489e-01, 5.77955910e-01, 6.57647244e-01, 8.36952073e-01,
+       9.31931351e-01, 1.12358425e+00]), scaled_genome=None, log_likelihood=7249.254644005687, fitness_values=array([2.51014209, 0.94646   , 0.82676501, 0.67692473, 0.55864534,
+       0.43335835, 0.34883756, 0.23857895, 0.17795986, 0.12601123]))]"
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+       0.4377187 , 0.40705929, 0.37904677, 0.36566539, 0.35067177])), Solution(fitness=0.39854758640777477, genome=array([ 0.02978764,  0.02694393,  0.03191011,  0.03226635,  0.04618912,
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+        0.02979562,  0.07061401,  0.02289995,  0.0548837 ,  0.04238755,
+        0.03489618,  0.05151178,  0.02586722,  0.04686359,  0.04061388,
+       -0.00311804,  0.11619232,  0.22050185,  0.30090417,  0.46395794,
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+       0.42339715, 0.42203032, 0.41657846, 0.4146428 , 0.39854759])), Solution(fitness=0.376195112488305, genome=array([0.02788906, 0.03814403, 0.03802506, 0.03847817, 0.20062677,
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+       0.04271344, 0.0688951 , 0.0338207 , 0.08780117, 0.02736449,
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+       0.58882055, 0.7108504 , 0.83899602, 0.94352038, 1.01316769]), scaled_genome=None, log_likelihood=4555.573164416036, fitness_values=array([0.73366239, 0.55070837, 0.47991326, 0.45851041, 0.43547375,
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+        0.03412902,  0.05379795,  0.03198202,  0.03837655,  0.03756591,
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+       -0.00207456,  0.12876044,  0.22595817,  0.33830432,  0.46696022,
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+       0.59238811, 0.7306453 , 0.83645314, 0.94029795, 1.00631142]), scaled_genome=None, log_likelihood=4507.37872176598, fitness_values=array([0.86915874, 0.68842682, 0.60859417, 0.55638481, 0.52951174,
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+       0.09010723, 0.18208459, 0.28857463, 0.34534915, 0.48927567,
+       0.61263291, 0.66339366, 0.77488784, 0.85464736, 0.98999229]), scaled_genome=None, log_likelihood=8909.3586152972, fitness_values=array([1.59643853, 0.97555854, 0.91994457, 0.62555445, 0.44374942,
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+       0.30759819, 0.23341372, 0.18610675, 0.15036979, 0.12312714])), Solution(fitness=0.12553139176805025, genome=array([0.06792181, 0.080227  , 0.06563248, 0.0395171 , 0.03004162,
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+       0.01340833, 0.0408152 , 0.01406218, 0.04562206, 0.02568357,
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+       0.60484883, 0.66233122, 0.77499858, 0.85617139, 0.98924449]), scaled_genome=None, log_likelihood=8763.000939288166, fitness_values=array([1.96065747, 1.3560662 , 0.94316594, 0.71805318, 0.60261015,
+       0.41963661, 0.31957321, 0.20422156, 0.15481354, 0.12553139])), Solution(fitness=0.10038285056650746, genome=array([0.07249025, 0.07676174, 0.06972389, 0.03047942, 0.03      ,
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+       0.00910563, 0.04251379, 0.00437221, 0.02808489, 0.02091837,
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+       0.3818203 , 0.29194524, 0.22369502, 0.15110818, 0.10038285])), Solution(fitness=0.120917850098064, genome=array([0.07081485, 0.07726656, 0.06774318, 0.03168981, 0.0300617 ,
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+       0.01263295, 0.0374599 , 0.01572504, 0.0516607 , 0.02321166,
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+       0.28695957, 0.23154535, 0.17583004, 0.12777845, 0.07777073])), Solution(fitness=0.12901780718307726, genome=array([0.07149865, 0.08762196, 0.07053307, 0.03445842, 0.03094853,
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+       0.32970955, 0.26085657, 0.2054229 , 0.162916  , 0.12901781])), Solution(fitness=0.13190978458649621, genome=array([0.06615377, 0.0871373 , 0.07571411, 0.03012304, 0.03001727,
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+       0.32931833, 0.24442606, 0.17043937, 0.1168075 , 0.0772443 ]))]"
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+       0.57253539, 0.36582114, 0.36582114, 0.323873  , 0.27361977])), Solution(fitness=0.2165939268752796, genome=array([0.07924339, 0.0885529 , 0.09758129, 0.03032598, 0.03963298,
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+       0.02041144, 0.04139905, 0.0188632 , 0.01057714, 0.01693773,
+       0.02141415, 0.0251654 , 0.00704375, 0.03036791, 0.02142769,
+       0.09588997, 0.19459851, 0.29792761, 0.37656146, 0.52673869,
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+       0.5121983 , 0.37794497, 0.34689226, 0.29605181, 0.21659393])), Solution(fitness=0.24678071851448669, genome=array([0.06659371, 0.06000096, 0.13451054, 0.04325001, 0.03      ,
+       0.0378115 , 0.10121839, 0.1897117 , 0.22298657, 0.27798368,
+       0.01662015, 0.02959571, 0.01831   , 0.01408784, 0.01160592,
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+       0.09082109, 0.17833875, 0.28828095, 0.36946113, 0.4796234 ,
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+       0.47223409, 0.37402416, 0.34835686, 0.31604964, 0.24678072])), Solution(fitness=0.2536519310398998, genome=array([0.07470635, 0.08069711, 0.10863498, 0.03192959, 0.03      ,
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+       0.54615488, 0.47690684, 0.37639508, 0.34716513, 0.314802  ])), Solution(fitness=0.2616063073109734, genome=array([0.08101926, 0.05648638, 0.08237284, 0.05305656, 0.03      ,
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+       1.17219613, 0.9091703 , 0.76841118, 0.45063891, 0.2447608 ]))]"
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+       1.49005667, 1.49005667, 1.19368983, 1.19368983, 1.19368983]))]"
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+       0.59746219, 0.66849197, 0.81624783, 0.95018916, 1.039018  ]), scaled_genome=None, log_likelihood=6006.4876735928365, fitness_values=array([3.16993326, 1.54546909, 1.03360327, 0.7409837 , 0.52810113,
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+       0.40370798, 0.29032177, 0.20236478, 0.15239209, 0.12661035]))]"
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+       0.65041582, 0.72602361, 0.80899156, 0.93657222, 1.01513227]), scaled_genome=None, log_likelihood=5021.370349200592, fitness_values=array([1.56584337, 1.00508309, 0.6101669 , 0.47724422, 0.32731153,
+       0.27891754, 0.21878373, 0.166053  , 0.13201223, 0.10999299])), Solution(fitness=0.11111030671550617, genome=array([0.03021809, 0.03013957, 0.10098918, 0.11216019, 0.26026507,
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+       0.07784323, 0.14177474, 0.34931164, 0.4341791 , 0.52260449,
+       0.62385309, 0.72593278, 0.80987775, 0.92858158, 0.99771404]), scaled_genome=None, log_likelihood=5084.972781737339, fitness_values=array([1.80392627, 1.09809221, 0.76533895, 0.51968945, 0.42759755,
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+       0.08991532, 0.16479782, 0.34865685, 0.4348916 , 0.52440368,
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+       0.36459077, 0.27917741, 0.19556684, 0.14354266, 0.10876833])), Solution(fitness=0.10177555022000044, genome=array([0.03572348, 0.03464724, 0.09935699, 0.11243052, 0.28800699,
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+       0.08873668, 0.03896091, 0.02398714, 0.01330413, 0.02808432,
+       0.01934682, 0.01475014, 0.01823181, 0.03473379, 0.034663  ,
+       0.03419966, 0.13643408, 0.34921322, 0.43454756, 0.52594094,
+       0.65073557, 0.72495652, 0.81111575, 0.93006204, 0.99681084]), scaled_genome=None, log_likelihood=4930.872912255929, fitness_values=array([1.63162251, 0.92134325, 0.53873719, 0.46145097, 0.37869689,
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+       0.25020074, 0.18786876, 0.1500592 , 0.11049993, 0.09418769])), Solution(fitness=0.09308098436018265, genome=array([0.03064979, 0.03056875, 0.10211116, 0.11639774, 0.30295768,
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+       0.0221868 , 0.0371069 , 0.02065358, 0.01341716, 0.02654038,
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+       0.02503527, 0.01356121, 0.01752212, 0.0379432 , 0.03903241,
+       0.0993768 , 0.18225084, 0.3492207 , 0.43397213, 0.52584095,
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+       0.27311935, 0.20120704, 0.1587101 , 0.12264842, 0.10411469]))]"
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+       0.60268541, 0.72517025, 0.81358369, 0.94649734, 1.03294862]), scaled_genome=None, log_likelihood=4896.626754370688, fitness_values=array([1.44189523, 1.33389115, 0.99030977, 0.5916609 , 0.5916609 ,
+       0.50964692, 0.45338968, 0.40817113, 0.34275067, 0.31401837])), Solution(fitness=0.26869379852100495, genome=array([0.03      , 0.03      , 0.18841914, 0.11024619, 0.25858825,
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+       0.08284391, 0.1789154 , 0.34678773, 0.43888559, 0.5165083 ,
+       0.61194791, 0.72974967, 0.81429055, 0.92362035, 1.01824888]), scaled_genome=None, log_likelihood=5126.67231054474, fitness_values=array([1.40637507, 1.04830972, 0.83313088, 0.57724182, 0.53584441,
+       0.46211687, 0.38173295, 0.34810357, 0.30239463, 0.2686938 ])), Solution(fitness=0.2618158021754441, genome=array([0.03      , 0.05177364, 0.11466313, 0.14205671, 0.27420076,
+       0.13370283, 0.20747037, 0.10620275, 0.11848724, 0.05933019,
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+       0.04582245, 0.02328206, 0.01666774, 0.04271971, 0.02339416,
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+       0.63452829, 0.73269222, 0.81928729, 0.9359654 , 1.0216989 ]), scaled_genome=None, log_likelihood=3505.996257411093, fitness_values=array([0.49594318, 0.36328211, 0.35163013, 0.28935918, 0.27512386,
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+       0.03669476, 0.01413985, 0.01779552, 0.03682008, 0.0428184 ,
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+       0.0917058 , 0.16884653, 0.34590152, 0.43435229, 0.52239853,
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+       0.4360559 , 0.32871169, 0.28449133, 0.17808481, 0.12366459])), Solution(fitness=0.10965063814065625, genome=array([0.03181813, 0.03012843, 0.09559718, 0.11779684, 0.2836041 ,
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+       0.07896597, 0.18760432, 0.11217085, 0.10229042, 0.03968383,
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+       0.08268608, 0.14427899, 0.34820767, 0.43328186, 0.52463299,
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+        6.36297797e-02,  4.00760809e-02,  1.27231888e-01,  1.00143143e-01,
+        1.90865582e-01,  8.16824936e-02,  2.61220210e-02,  5.58616726e-02,
+        4.08716235e-03,  3.09419443e-02,  1.76921223e-02,  4.72233741e-02,
+        3.82280957e-02,  4.77778751e-02,  1.66269094e-02,  2.85102747e-02,
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+        4.07925244e-01,  5.47892888e-01,  6.56435667e-01,  7.99314828e-01,
+        9.49727205e-01,  1.09711644e+00]), scaled_genome=None, log_likelihood=11172.334253291428, fitness_values=array([5.80708834, 2.37463568, 2.37463568, 1.85442673, 1.56500967,
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+        0.06750244,  0.12704986,  0.09657131,  0.15631247,  0.03      ,
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+       -0.16528934,  0.02594835,  0.15435866,  0.25752274,  0.385184  ,
+        0.50510855,  0.6607382 ,  0.81358409,  0.9411699 ,  1.09509762]), scaled_genome=None, log_likelihood=10506.136308401976, fitness_values=array([4.03632546, 3.58505704, 1.69653972, 1.69653972, 1.34685148,
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+        0.02250433,  0.04744389,  0.00444434,  0.02348108,  0.02895514,
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+       1.33079437, 1.06175501, 0.8815949 , 0.81626429, 0.62983515])), Solution(fitness=0.7099218034068486, genome=array([ 0.07316828,  0.08058654,  0.25518533,  0.22587747,  0.0742034 ,
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+        0.49710608,  0.62377097,  0.71558991,  0.83772881,  0.9625881 ]), scaled_genome=None, log_likelihood=5105.140784721137, fitness_values=array([1.88775961, 0.97349734, 0.87757014, 0.57162781, 0.4603649 ,
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+       0.35813124, 0.23038266, 0.14449359, 0.07239035, 0.03347407]))]"
+1,synthetic_10_32,DESolver,0.9175667439788047,0.17333964869693094,"[1.13192451 0.88708148 0.98415429 1.23454339 0.82520047 0.9521362
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+       0.12446345, 0.22359565, 0.40130518, 0.50757362, 0.57972923,
+       0.70223134, 0.78650787, 0.87593642, 0.97178831, 1.09140394]), scaled_genome=None, log_likelihood=8118.614630958004, fitness_values=array([1.94226941, 1.69374957, 1.14619111, 1.00809798, 0.72445579,
+       0.6963498 , 0.53292218, 0.45448302, 0.37219087, 0.31055792])), Solution(fitness=0.4541992796813866, genome=array([0.13652844, 0.06488816, 0.05230709, 0.24118451, 0.32787766,
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+       0.02820803, 0.03308509, 0.04220132, 0.00774289, 0.035574  ,
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+       0.69425282, 0.6893015 , 0.43769107, 0.43769107, 0.36324287])), Solution(fitness=0.2828599454695877, genome=array([0.15928748, 0.06446268, 0.18848384, 0.04813825, 0.28335718,
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+       0.51935883, 0.51935883, 0.36017261, 0.33626829, 0.29178212]))]"
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+       0.7056686 , 0.4413803 , 0.38385051, 0.1980599 , 0.07476904]))]"
+0,synthetic_10_27,iLSHADESolver,0.6947811928767967,0.14104853045605054,"[0.74971914 0.71970067 0.68592105 0.71178699 0.70109029 0.72329372
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+       3.23059058, 2.56790127, 2.56790127, 2.56790127, 2.43670886])), Solution(fitness=2.69420617008033, genome=array([0.09096175, 0.23447313, 0.23974634, 0.13349071, 0.12340332,
+       0.15674696, 0.14437911, 0.13704859, 0.14208901, 0.2202268 ,
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+       3.97530348, 3.42637127, 2.69420617, 2.69420617, 2.69420617]))]"
+2,synthetic_10_27,SHADESolver,1.1739393093038784,0.08386417274947115,"[1.14223794 1.02877383 1.23454627 1.23875677 1.15718173 1.08984899
+ 1.06975291 1.24885325 1.25847283 1.27096857]",0.0,"[Solution(fitness=1.1422379377957446, genome=array([ 0.08863578,  0.20032869,  0.09030889,  0.14232294,  0.14942906,
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+        0.4620927 ,  0.55408281,  0.67066703,  0.72183117,  0.85227945]), scaled_genome=None, log_likelihood=6795.565699507526, fitness_values=array([4.81495055, 2.76448827, 2.1566789 , 2.05488406, 1.84308747,
+       1.62273381, 1.32770114, 1.32770114, 1.23337727, 1.14223794])), Solution(fitness=1.0287738338414487, genome=array([ 0.07771344,  0.21062185,  0.03940018,  0.15223727,  0.14617697,
+        0.09409142,  0.07241529,  0.07083796,  0.12442448,  0.45754713,
+        0.02378777,  0.00799341,  0.05350494,  0.02549934,  0.01881321,
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+        0.45058797,  0.56715926,  0.6709185 ,  0.72504139,  0.84895727]), scaled_genome=None, log_likelihood=6426.718617447334, fitness_values=array([3.94008403, 2.24984456, 2.2373292 , 1.50115501, 1.50115501,
+       1.38391911, 1.24154155, 1.12694374, 1.12190884, 1.02877383])), Solution(fitness=1.2345462749853808, genome=array([0.22370094, 0.07240678, 0.07785842, 0.08883805, 0.18163617,
+       0.11246584, 0.03      , 0.07261949, 0.1710098 , 0.36752602,
+       0.00958634, 0.01682203, 0.02250136, 0.03107101, 0.034273  ,
+       0.02923286, 0.01849435, 0.0213158 , 0.02807255, 0.04028969,
+       0.00927967, 0.0499785 , 0.14587931, 0.24374207, 0.3453818 ,
+       0.47486421, 0.57304291, 0.67966115, 0.74337029, 0.86435956]), scaled_genome=None, log_likelihood=5441.607611141703, fitness_values=array([4.61311614, 3.63581261, 2.65426998, 2.07349584, 1.86116312,
+       1.62093625, 1.43578218, 1.3851422 , 1.3851422 , 1.23454627])), Solution(fitness=1.2387567696700617, genome=array([0.17092939, 0.13414272, 0.08613457, 0.10386071, 0.13742934,
+       0.12434942, 0.03      , 0.15813236, 0.144954  , 0.29060567,
+       0.01498341, 0.02317165, 0.04038544, 0.02039635, 0.01604228,
+       0.02341243, 0.03354681, 0.0321598 , 0.0224187 , 0.03543803,
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+       0.44266029, 0.5489098 , 0.67566138, 0.74401702, 0.8574072 ]), scaled_genome=None, log_likelihood=5824.581387007619, fitness_values=array([4.80165054, 2.23522821, 2.22086584, 2.18025906, 1.73314046,
+       1.73314046, 1.57884755, 1.40962674, 1.28815345, 1.23875677])), Solution(fitness=1.1571817349039195, genome=array([ 0.05913167,  0.21265424,  0.10559951,  0.21694623,  0.14386486,
+        0.08852879,  0.03      ,  0.20132177,  0.1124732 ,  0.36856909,
+        0.0153187 ,  0.00722501,  0.03358405,  0.02538208,  0.0392939 ,
+        0.03146113,  0.01588961,  0.02332018,  0.02317005,  0.04405959,
+       -0.08204263,  0.02033957,  0.13317994,  0.23785607,  0.35605929,
+        0.44586547,  0.53230642,  0.67084567,  0.73486373,  0.85305803]), scaled_genome=None, log_likelihood=7369.117289410949, fitness_values=array([5.19545541, 2.75238342, 2.12163221, 2.00361474, 1.88743078,
+       1.74552694, 1.48632407, 1.3452766 , 1.25304258, 1.15718173])), Solution(fitness=1.089848989064712, genome=array([ 0.04351073,  0.28047785,  0.05770042,  0.11406109,  0.16203631,
+        0.12449005,  0.08893745,  0.11078464,  0.14651674,  0.376562  ,
+        0.03550605,  0.01004425,  0.03262926,  0.02641821,  0.02541972,
+        0.02637838,  0.02312708,  0.04044087,  0.02302971,  0.04780842,
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+        0.45117216,  0.53473928,  0.64642489,  0.71919933,  0.86043304]), scaled_genome=None, log_likelihood=7515.356191405637, fitness_values=array([5.88914931, 3.35046926, 2.51729923, 2.12490404, 1.80895235,
+       1.64745185, 1.550981  , 1.25423967, 1.19581081, 1.08984899])), Solution(fitness=1.0697529095305331, genome=array([ 0.03233671,  0.17656866,  0.03      ,  0.10964887,  0.1076603 ,
+        0.10276002,  0.0834363 ,  0.12280322,  0.16288384,  0.42988333,
+        0.01659379,  0.00654745,  0.02529928,  0.0259729 ,  0.03148096,
+        0.04762712,  0.0194269 ,  0.01376247,  0.03577514,  0.03622443,
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+       1.50267172, 1.34291243, 1.1237496 , 1.1237496 , 1.06975291])), Solution(fitness=1.248853248385103, genome=array([0.17452333, 0.14097841, 0.07347344, 0.09569946, 0.16240261,
+       0.04749267, 0.08990453, 0.16550675, 0.10081361, 0.28376607,
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+       0.0119673 , 0.04608098, 0.16797057, 0.22617377, 0.3480714 ,
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+       1.6294564 , 1.57365853, 1.44121017, 1.38396546, 1.27096857]))]"
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+       1.42434207, 1.23580244, 1.02569089, 1.02569089, 1.02569089])), Solution(fitness=0.8705301260800634, genome=array([ 0.03      ,  0.09361211,  0.04362875,  0.19027657,  0.21346718,
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+       0.50432899, 0.40934414, 0.36614697, 0.32648478, 0.2646468 ]))]"
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+       0.4949144 , 0.38059512, 0.24919596, 0.15423347, 0.07040543]))]"
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+       0.62277996, 0.62277996, 0.59331849, 0.55293308, 0.41863112])), Solution(fitness=0.39364232589583537, genome=array([0.10082554, 0.0553474 , 0.10392533, 0.11056414, 0.18464238,
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+       0.57991755, 0.6780291 , 0.72661048, 0.89411835, 0.9940405 ]), scaled_genome=None, log_likelihood=5989.631447172757, fitness_values=array([1.55435706, 1.29427408, 0.97771736, 0.89961105, 0.68860367,
+       0.65687894, 0.51023571, 0.49682933, 0.44916722, 0.38486358])), Solution(fitness=0.4714558733900898, genome=array([0.06155055, 0.03      , 0.08932499, 0.10464163, 0.1865688 ,
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+       0.73005379, 0.68135577, 0.58774248, 0.53670084, 0.47145587])), Solution(fitness=0.43175908147363196, genome=array([0.06011296, 0.09760311, 0.05247037, 0.135179  , 0.14394844,
+       0.23275987, 0.22881118, 0.03314906, 0.1153152 , 0.07285779,
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+       0.65883325, 0.57113051, 0.54621662, 0.46732339, 0.43175908]))]"
+3,synthetic_10_36,GASolver,0.33969109753890664,0.0753192642275706,"[0.34834673 0.38228037 0.30807352 0.27392827 0.36632114 0.18876609
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+       0.0270598 , 0.1238012 , 0.23924701, 0.30890582, 0.4923156 ,
+       0.57093199, 0.6891176 , 0.88228807, 0.98924547, 1.06742469]), scaled_genome=None, log_likelihood=4253.638802689368, fitness_values=array([0.71561232, 0.60448719, 0.53690846, 0.49742335, 0.44842107,
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+        0.14383141,  0.26925448,  0.18765101,  0.06112554,  0.11044376,
+        0.06618054,  0.04321737,  0.05231077,  0.00623975,  0.05226708,
+        0.02329961,  0.05765983,  0.03130107,  0.01036147,  0.0242613 ,
+       -0.03125594,  0.07414548,  0.20708471,  0.30134278,  0.42323793,
+        0.51625852,  0.59945862,  0.70803626,  0.88962122,  0.99441894]), scaled_genome=None, log_likelihood=4500.490436968351, fitness_values=array([0.88907918, 0.67609273, 0.54008792, 0.48673784, 0.41196838,
+       0.3720541 , 0.36585259, 0.34492388, 0.32425792, 0.30807352])), Solution(fitness=0.2739282651017066, genome=array([ 0.04542978,  0.04635364,  0.03944774,  0.08037531,  0.23634165,
+        0.13491482,  0.21831388,  0.03618024,  0.06657128,  0.09607165,
+        0.05234596,  0.03639083,  0.0671724 ,  0.01123904,  0.02846846,
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+        0.10434145,  0.22263082,  0.06406902,  0.07646478,  0.05133469,
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+        0.02101722,  0.02190275,  0.01748607,  0.01867688,  0.04492362,
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+       0.03138389, 0.0344237 , 0.04147396, 0.01361002, 0.032147  ,
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+       0.55404505, 0.45672227, 0.39724184, 0.37456448, 0.331926  ])), Solution(fitness=0.35424769704083836, genome=array([0.03950484, 0.04862168, 0.08532724, 0.19180997, 0.22094777,
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+       0.13236568, 0.27564164, 0.34030317, 0.39138455, 0.50481291,
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+       0.05074018, 0.04259923, 0.00844748, 0.010557  , 0.00617757,
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+       0.54566405, 0.45306168, 0.39407602, 0.32818078, 0.30246199]))]"
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+       0.57168217, 0.51125165, 0.49884947, 0.4772756 , 0.47083333]))]"
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+       0.0072812 , 0.11034061, 0.21282306, 0.35143282, 0.48249622,
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+       0.81793196, 0.75595862, 0.73194398, 0.7169204 , 0.6790115 ]))]"
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+       0.64287734, 0.4901816 , 0.40315497, 0.22928571, 0.09530196]))]"
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+        7.22568109e-02,  1.13139304e-01,  3.96115642e-02,  7.46590960e-02,
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+        0.04063406,  0.02430129,  0.02558806,  0.0391734 ,  0.01562246,
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+        0.04487524,  0.02640272,  0.04149627,  0.02822362,  0.0153844 ,
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+        0.02924414,  0.01204614,  0.03775169,  0.02089922,  0.0376447 ,
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+       -0.02784741,  0.04603189,  0.11406749,  0.32034739,  0.39830533,
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+       0.0397771 , 0.00692078, 0.01896415, 0.0381991 , 0.0260413 ,
+       0.0248192 , 0.03414955, 0.02512489, 0.04415906, 0.0118886 ,
+       0.10438527, 0.20044596, 0.30331715, 0.37448331, 0.47430653,
+       0.55419134, 0.66507233, 0.76106318, 0.81942032, 0.94368826]), scaled_genome=None, log_likelihood=5297.495650283267, fitness_values=array([5.10331357, 4.19361511, 2.1346276 , 1.95197121, 1.95197121,
+       1.59529747, 1.12765742, 1.12765742, 0.71855913, 0.71855913])), Solution(fitness=1.0370136050761067, genome=array([0.26114408, 0.09829012, 0.11202429, 0.08265485, 0.06117813,
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+       0.02862435, 0.02233594, 0.01411474, 0.01337984, 0.03647176,
+       0.02118075, 0.02986517, 0.03244139, 0.05305563, 0.01488453,
+       0.0800983 , 0.21854348, 0.33003692, 0.35982263, 0.47052364,
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+       1.21481139, 1.06552728, 1.06552728, 1.06223517, 1.03701361])), Solution(fitness=0.7880289001178573, genome=array([0.07323692, 0.13418132, 0.1019979 , 0.098497  , 0.19738573,
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+       0.56408254, 0.65096095, 0.7169579 , 0.81364387, 0.9515227 ]), scaled_genome=None, log_likelihood=4721.869761729386, fitness_values=array([4.3010983 , 3.77077208, 2.44148369, 1.71096875, 1.27969395,
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+       0.01676366, 0.02333564, 0.02739844, 0.0153802 , 0.05280759,
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+        0.01853209,  0.05188946,  0.01950756,  0.02295794,  0.04759757,
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+        0.09970922,  0.04005293,  0.01327843,  0.096927  ,  0.02346999,
+        0.05317133,  0.02675719,  0.07998398,  0.04613513,  0.02747302,
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+       9.72805723e-01, 1.07636504e+00]), scaled_genome=None, log_likelihood=3360.235094380243, fitness_values=array([1.51533822, 0.82177355, 0.63896801, 0.47984015, 0.30282152,
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+       0.03666515, 0.03532876, 0.03287081, 0.01652746, 0.01842527,
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+       0.00332185, 0.19593534, 0.32399866, 0.42623251, 0.55263946,
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+       0.24199375, 0.13616973, 0.09701878, 0.0533475 , 0.03288203])), Solution(fitness=0.02791326685628641, genome=array([0.18432675, 0.03170977, 0.19929364, 0.19613096, 0.07489696,
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+       0.02001369, 0.03906649, 0.03630005, 0.01911295, 0.05379821,
+       0.00246992, 0.18565378, 0.32654869, 0.42712412, 0.54552741,
+       0.57140886, 0.77198428, 0.86591598, 0.97018032, 1.06641743]), scaled_genome=None, log_likelihood=2635.8448484855353, fitness_values=array([2.23450769, 0.90627086, 0.66051176, 0.48582166, 0.3735229 ,
+       0.23953593, 0.14150388, 0.08196261, 0.0460885 , 0.02791327]))]"
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+       0.43536976, 0.41213549, 0.30882868, 0.29813946, 0.21331539])), Solution(fitness=0.23173226327707142, genome=array([ 0.15894946,  0.04744496,  0.21621353,  0.15538368,  0.09930824,
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+       0.56955558, 0.45551118, 0.39080957, 0.30254067, 0.26415907])), Solution(fitness=0.2177944804015723, genome=array([ 0.17962649,  0.05846032,  0.18080675,  0.25649234,  0.15593312,
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+       0.42472583, 0.32739284, 0.31086372, 0.21852314, 0.21779448])), Solution(fitness=0.21556114828807385, genome=array([ 0.18613616,  0.06530098,  0.14442503,  0.22547624,  0.13858182,
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+       0.49094576, 0.38075056, 0.34039435, 0.25829001, 0.21556115]))]"
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+       9.87189533e-01, 1.08445169e+00]), scaled_genome=None, log_likelihood=2192.0133840311632, fitness_values=array([0.56888941, 0.4134713 , 0.27951215, 0.21436801, 0.20054625,
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+        0.0502996 ,  0.03380613,  0.04728746,  0.023307  ,  0.05439657,
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+       0.51051318, 0.38919355, 0.25877127, 0.12592672, 0.05114132])), Solution(fitness=0.058477747352221734, genome=array([2.02200598e-01, 4.41861608e-02, 1.63734130e-01, 2.26580442e-01,
+       1.32741463e-01, 3.42124701e-02, 9.57065496e-02, 3.36973965e-02,
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+       4.30037381e-04, 2.02996171e-01, 3.25044585e-01, 4.25657006e-01,
+       5.51601638e-01, 6.99638118e-01, 7.74352966e-01, 8.41773551e-01,
+       9.76492607e-01, 1.11380617e+00]), scaled_genome=None, log_likelihood=3399.182576890005, fitness_values=array([1.95532139, 1.63599992, 1.12282448, 0.69562591, 0.50356682,
+       0.49099091, 0.33462504, 0.27015281, 0.12155307, 0.05847775])), Solution(fitness=0.043585494512925094, genome=array([0.20399011, 0.03140539, 0.18795626, 0.21142238, 0.12048263,
+       0.03014557, 0.10667726, 0.04659733, 0.08819509, 0.03076847,
+       0.03809431, 0.02807428, 0.03239587, 0.01638369, 0.0185378 ,
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+       0.42803957, 0.42803957, 0.30576718, 0.14265892, 0.04358549]))]"
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+       0.20910756, 0.1661544 , 0.125158  , 0.11035061, 0.09370408])), Solution(fitness=0.05985014344055651, genome=array([ 0.07635007,  0.19136053,  0.16648907,  0.18729418,  0.04354257,
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+        0.04910707,  0.05349892,  0.02849206,  0.03563626,  0.00792132,
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+       0.1764118 , 0.13867914, 0.10608946, 0.0734635 , 0.05985014])), Solution(fitness=0.05621198153661549, genome=array([ 0.12554704,  0.1429606 ,  0.15802541,  0.20401849,  0.03598933,
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+        0.59113299,  0.76412822,  0.87069109,  1.00015752,  1.0988714 ]), scaled_genome=None, log_likelihood=103.06187504266438, fitness_values=array([1.28284109, 0.68503138, 0.49001721, 0.38812357, 0.253117  ,
+       0.19127765, 0.13695422, 0.12167698, 0.07589151, 0.05621198])), Solution(fitness=0.05827090874640717, genome=array([ 0.10056085,  0.17785527,  0.15394252,  0.19770473,  0.03768055,
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+        0.05641002,  0.049167  ,  0.0260327 ,  0.0381097 ,  0.00688136,
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+       0.17114505, 0.14930756, 0.12951231, 0.11042434, 0.09872298]))]"
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+       0.27362731, 0.2531969 , 0.21578675, 0.19138463, 0.18049301])), Solution(fitness=0.15939953311771612, genome=array([ 0.09774078,  0.19825703,  0.16442432,  0.12621088,  0.08476847,
+        0.09977638,  0.09358849,  0.16844331,  0.04088584,  0.03      ,
+        0.05054652,  0.04132972,  0.0292787 ,  0.03465558,  0.04066401,
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+       -0.06717143,  0.03807356,  0.16731343,  0.32060913,  0.41896228,
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+        0.04522094,  0.05085874,  0.03244398,  0.03095955,  0.04068608,
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+        0.58594057,  0.76212074,  0.86496504,  0.99772949,  1.1439115 ]), scaled_genome=None, log_likelihood=715.0977596078999, fitness_values=array([0.88017802, 0.75786219, 0.58529301, 0.4346091 , 0.36064624,
+       0.25771134, 0.2520624 , 0.2002395 , 0.18933856, 0.17212605])), Solution(fitness=0.166977970166133, genome=array([ 0.07643053,  0.20720185,  0.21115761,  0.15523693,  0.09506895,
+        0.07324919,  0.10559993,  0.1305226 ,  0.07773439,  0.03      ,
+        0.04754676,  0.04604627,  0.03710863,  0.03252408,  0.01894123,
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+       0.28073701, 0.24537726, 0.20791733, 0.18846335, 0.16697797])), Solution(fitness=0.18796264539412463, genome=array([ 0.05218173,  0.18037112,  0.1878017 ,  0.18380054,  0.09406517,
+        0.10931238,  0.09008417,  0.17315619,  0.06583416,  0.03838912,
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+       0.26913682, 0.21176623, 0.20466305, 0.17079717, 0.16183886]))]"
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+       1.36700062, 1.35004239, 1.35004239, 1.35004239, 1.3386611 ]))]"
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+       0.5568063 , 0.49288685, 0.42349   , 0.31717651, 0.26384135]))]"
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+       -0.11092146,  0.03022981,  0.13406579,  0.23102386,  0.33747937,
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+        0.01194282,  0.00757361,  0.0441378 ,  0.02899947,  0.02731857,
+        0.03008089,  0.01543037,  0.04291674,  0.00691855,  0.01888644,
+       -0.06764793,  0.03016794,  0.14501414,  0.23113826,  0.33509053,
+        0.49491249,  0.53157333,  0.6321039 ,  0.73623083,  0.86299688]), scaled_genome=None, log_likelihood=8239.34842123193, fitness_values=array([1.44465828, 1.44465828, 0.92846154, 0.92846154, 0.7206424 ,
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+       0.02505594, 0.01631752, 0.03814815, 0.00686156, 0.01853461,
+       0.02313253, 0.03074528, 0.153575  , 0.24338609, 0.34517337,
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+        0.02353839,  0.00728557,  0.06127565,  0.02954427,  0.02562593,
+        0.0253718 ,  0.01731377,  0.03853015,  0.0073358 ,  0.01879538,
+       -0.09102991,  0.02922848,  0.15560551,  0.2359941 ,  0.3388977 ,
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+       -0.08144469,  0.03023294,  0.15130309,  0.23623748,  0.3376998 ,
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+        0.03776446,  0.05430496,  0.07100764,  0.02235288,  0.02841972,
+        0.00865849,  0.04231486,  0.02676304,  0.03838115,  0.04707223,
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+       0.58875498, 0.63759757, 0.7561202 , 0.8142885 , 0.9388266 ]), scaled_genome=None, log_likelihood=5961.689929752698, fitness_values=array([2.672208  , 2.41523156, 2.41523156, 1.80757677, 1.80757677,
+       1.80639331, 1.80639331, 1.80639331, 1.69607579, 1.33720859])), Solution(fitness=1.6631023601454276, genome=array([0.19662296, 0.2205949 , 0.09545114, 0.03      , 0.10285057,
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+       0.01721527, 0.01731225, 0.03380429, 0.01325312, 0.01986782,
+       0.02632693, 0.03495682, 0.02523467, 0.01771202, 0.0100601 ,
+       0.1093649 , 0.24298128, 0.3180951 , 0.38902558, 0.46623277,
+       0.55168719, 0.6873094 , 0.77283835, 0.9027303 , 0.96063207]), scaled_genome=None, log_likelihood=7025.034804849541, fitness_values=array([2.851368  , 2.53072447, 2.53072447, 2.50259227, 2.50259227,
+       2.34535726, 1.88299881, 1.88299881, 1.66310236, 1.66310236])), Solution(fitness=1.7307881691985756, genome=array([0.11093679, 0.17675766, 0.12783201, 0.07611734, 0.09959816,
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+       0.01456695, 0.02033336, 0.03047461, 0.00474962, 0.02156679,
+       0.02704212, 0.01195321, 0.01347813, 0.04334296, 0.03257296,
+       0.10517431, 0.25324485, 0.3176828 , 0.41055484, 0.48822677,
+       0.59331981, 0.6516397 , 0.74386246, 0.81623751, 0.93511524]), scaled_genome=None, log_likelihood=5556.416488975197, fitness_values=array([3.09369832, 2.41975834, 2.41975834, 1.82559567, 1.82559567,
+       1.82559567, 1.73078817, 1.73078817, 1.73078817, 1.73078817])), Solution(fitness=1.2316925230460305, genome=array([0.25016786, 0.18290429, 0.10942975, 0.03      , 0.03      ,
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+       0.01235774, 0.01852922, 0.03153932, 0.01228271, 0.03015286,
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+       0.10795866, 0.26451274, 0.30947725, 0.42898481, 0.46849802,
+       0.5709152 , 0.65281436, 0.76427794, 0.84523749, 0.95500673]), scaled_genome=None, log_likelihood=8355.851210812656, fitness_values=array([4.05344709, 1.42927207, 1.42927207, 1.42927207, 1.42927207,
+       1.42927207, 1.42927207, 1.42927207, 1.42927207, 1.23169252])), Solution(fitness=1.5257791365979478, genome=array([0.21086023, 0.25928012, 0.19134991, 0.03      , 0.04588568,
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+       0.02172336, 0.03203671, 0.01128383, 0.02611665, 0.00806527,
+       0.00099629, 0.01608837, 0.03111396, 0.00863719, 0.03248167,
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+       0.02451709, 0.01332323, 0.02162006, 0.00930259, 0.02097208,
+       0.00779455, 0.01481638, 0.0243425 , 0.04613159, 0.02103428,
+       0.09107654, 0.24372819, 0.28699297, 0.38764513, 0.47308273,
+       0.578981  , 0.63203993, 0.77332965, 0.8555592 , 0.9873721 ]), scaled_genome=None, log_likelihood=8828.80273696082, fitness_values=array([2.73919793, 2.32355657, 2.32355657, 2.32355657, 1.69398828,
+       1.69398828, 1.69398828, 1.69398828, 1.66086921, 1.66086921])), Solution(fitness=1.284857318455312, genome=array([0.29696495, 0.19255602, 0.30528211, 0.12753663, 0.03797163,
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+       0.0097108 , 0.02612314, 0.03908915, 0.00099629, 0.01629677,
+       0.01899453, 0.02231539, 0.01801115, 0.00606875, 0.03403637,
+       0.10975586, 0.21555953, 0.2862387 , 0.41528308, 0.46602621,
+       0.54936546, 0.63672407, 0.76206039, 0.80781807, 0.95127577]), scaled_genome=None, log_likelihood=9009.115911680015, fitness_values=array([1.79751659, 1.79751659, 1.79751659, 1.32300621, 1.32300621,
+       1.28485732, 1.28485732, 1.28485732, 1.28485732, 1.28485732])), Solution(fitness=1.7698811019755885, genome=array([0.23165402, 0.36197335, 0.03010333, 0.03      , 0.03      ,
+       0.04067626, 0.09066008, 0.18805535, 0.26091066, 0.1469669 ,
+       0.02872631, 0.01955938, 0.02363844, 0.00861849, 0.02271991,
+       0.00420868, 0.04063385, 0.0252692 , 0.01914961, 0.03468717,
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+       1.7698811 , 1.7698811 , 1.7698811 , 1.7698811 , 1.7698811 ])), Solution(fitness=1.5117901037133818, genome=array([0.19110422, 0.15445978, 0.14891405, 0.16249486, 0.17725779,
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+       0.01775783, 0.01230995, 0.01762693, 0.03652918, 0.02690212,
+       0.0072048 , 0.01435654, 0.04504015, 0.02154502, 0.02106396,
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+       2.09606042, 1.86210187, 1.8027212 , 1.5117901 , 1.5117901 ])), Solution(fitness=1.2415982050612007, genome=array([0.21108138, 0.04395336, 0.35297961, 0.03156103, 0.15565104,
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+       0.00689085, 0.03784838, 0.02247019, 0.018907  , 0.01281592,
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+       0.11423148, 0.20454488, 0.27758931, 0.35074539, 0.45322695,
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+       1.24159821, 1.24159821, 1.24159821, 1.24159821, 1.24159821]))]"
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+        0.59180242,  0.66250011,  0.86993376,  1.02800556,  1.26648748]), scaled_genome=None, log_likelihood=2959.419536032192, fitness_values=array([0.90545078, 0.77665256, 0.54771559, 0.40149322, 0.35442101,
+       0.27573393, 0.22680087, 0.20320769, 0.19049509, 0.17639056])), Solution(fitness=0.1491894753741439, genome=array([ 0.03044934,  0.03660684,  0.03107708,  0.10697194,  0.21437708,
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+       0.31245717, 0.26967459, 0.24355855, 0.1872286 , 0.14918948]))]"
+1,synthetic_10_73,DESolver,0.7363084125730825,0.09705801131889498,"[0.71764537 0.85801239 0.75679362 0.54043568 0.80178172 0.86986143
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+       0.86057233, 0.86057233, 0.82845988, 0.787282  , 0.70995815]))]"
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+       0.43232471, 0.35379537, 0.33367077, 0.2874311 , 0.27162349])), Solution(fitness=0.25947004573440724, genome=array([ 0.03833331,  0.03640743,  0.03      ,  0.09200531,  0.20389993,
+        0.1404741 ,  0.19837759,  0.19738941,  0.08198387,  0.03322121,
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+       0.35641452, 0.34922207, 0.27071166, 0.27071166, 0.25947005]))]"
+3,synthetic_10_73,GASolver,0.24244933692545284,0.059210495122567985,"[0.36017072 0.15213301 0.25444417 0.23850553 0.20933656 0.30643084
+ 0.29563055 0.19425825 0.20633669 0.20724705]",0.0,"[Solution(fitness=0.3601707181425584, genome=array([ 0.03931464,  0.04270431,  0.03244075,  0.03450215,  0.14261889,
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+       0.56545955, 0.72507881, 0.78076062, 0.88396482, 1.03537958]), scaled_genome=None, log_likelihood=7748.821704551595, fitness_values=array([2.79654751, 1.65479648, 1.3442099 , 1.10912833, 0.804604  ,
+       0.77879062, 0.68370754, 0.58837319, 0.5002332 , 0.47502933])), Solution(fitness=0.40115777454983054, genome=array([0.05774561, 0.11412047, 0.16357917, 0.26804711, 0.13121918,
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+       0.0321781 , 0.02466908, 0.01981265, 0.01964027, 0.02479664,
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+       0.11077823, 0.19075786, 0.25325555, 0.37437338, 0.492986  ,
+       0.56945958, 0.72862763, 0.85760326, 0.89642605, 1.03776068]), scaled_genome=None, log_likelihood=6556.297994928184, fitness_values=array([2.79460816, 1.72667077, 1.30446462, 1.09516488, 0.72515935,
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+       0.56236704, 0.70664095, 0.76140231, 0.88240146, 1.0310214 ]), scaled_genome=None, log_likelihood=6806.1006533395885, fitness_values=array([2.05484116, 1.53487102, 0.92232496, 0.89563525, 0.65973002,
+       0.65037606, 0.41662518, 0.37472093, 0.37472093, 0.33878944])), Solution(fitness=0.4143817367106436, genome=array([0.04695298, 0.13608462, 0.16696289, 0.25808782, 0.13452036,
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+       0.71618914, 0.60572987, 0.58097931, 0.47577662, 0.41438174])), Solution(fitness=0.44197971697397115, genome=array([0.099329  , 0.17909633, 0.13863436, 0.16990547, 0.09963636,
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+        0.48504474,  0.64268201,  0.7527125 ,  0.90179035,  1.10908927]), scaled_genome=None, log_likelihood=5703.253761184748, fitness_values=array([2.5511393 , 1.2643712 , 1.2643712 , 0.89959584, 0.89959584,
+       0.89959584, 0.89959584, 0.89959584, 0.89959584, 0.6335914 ])), Solution(fitness=0.9147740085702444, genome=array([ 0.12607292,  0.19081129,  0.03869843,  0.05682243,  0.03      ,
+        0.1132212 ,  0.157344  ,  0.06820819,  0.1743129 ,  0.0717371 ,
+        0.01695647,  0.03233764,  0.03836754,  0.02465646,  0.02892568,
+        0.02903784,  0.02174606,  0.02808135,  0.04254868,  0.01785091,
+       -0.13402053,  0.00152781,  0.083533  ,  0.21717477,  0.34712112,
+        0.4727788 ,  0.65605084,  0.73504447,  0.86425984,  0.99964737]), scaled_genome=None, log_likelihood=872.8456146719428, fitness_values=array([2.04429954, 1.57780588, 1.57780588, 1.57780588, 1.16826808,
+       0.91477401, 0.91477401, 0.91477401, 0.91477401, 0.91477401])), Solution(fitness=0.7118037444034886, genome=array([ 0.03      ,  0.21242843,  0.17060658,  0.07173372,  0.13545642,
+        0.11372461,  0.09677275,  0.12977069,  0.18365823,  0.05533968,
+        0.01346254,  0.01952727,  0.05215481,  0.02882542,  0.04922891,
+        0.04324784,  0.02634293,  0.03113834,  0.05398307,  0.06870758,
+       -0.11613505, -0.0100473 ,  0.05281886,  0.20872893,  0.36554162,
+        0.50207272,  0.61342155,  0.6715033 ,  0.84000845,  0.96960305]), scaled_genome=None, log_likelihood=2715.363087953959, fitness_values=array([1.52147562, 1.52147562, 1.52147562, 1.52147562, 1.52147562,
+       1.40732973, 1.40732973, 1.19819114, 0.98741482, 0.71180374])), Solution(fitness=0.9101759263836258, genome=array([ 0.25542906,  0.25506896,  0.05735905,  0.06216244,  0.05007654,
+        0.18054281,  0.17623478,  0.1561589 ,  0.11855811,  0.19486435,
+        0.0371842 ,  0.02262747,  0.05597469,  0.0193453 ,  0.03031797,
+        0.04406449,  0.02004427,  0.03304221,  0.04148695,  0.04180218,
+       -0.17768746,  0.01261811,  0.08715588,  0.20982656,  0.33038106,
+        0.46703174,  0.59879003,  0.65129224,  0.75350567,  0.95051034]), scaled_genome=None, log_likelihood=3539.901562506851, fitness_values=array([2.00365173, 2.00365173, 1.62167865, 1.56793048, 1.48023479,
+       1.43997409, 0.91017593, 0.91017593, 0.91017593, 0.91017593])), Solution(fitness=0.7009295731066724, genome=array([ 0.03901536,  0.24597025,  0.05066985,  0.21613604,  0.05175099,
+        0.03      ,  0.17044855,  0.0986199 ,  0.25600317,  0.21252497,
+        0.04445639,  0.03432326,  0.02061143,  0.02466404,  0.06301496,
+        0.03150855,  0.01808271,  0.0225305 ,  0.04634194,  0.01805141,
+       -0.15831063,  0.00323485,  0.10324793,  0.18590525,  0.38870292,
+        0.56766327,  0.66040563,  0.76878076,  0.93026961,  1.06394012]), scaled_genome=None, log_likelihood=3860.2129406826894, fitness_values=array([1.79340057, 1.56051693, 1.56051693, 1.55874454, 1.4203698 ,
+       1.02107562, 0.76644049, 0.76644049, 0.70092957, 0.70092957])), Solution(fitness=0.8280798890822444, genome=array([ 0.03516625,  0.28058476,  0.16690103,  0.12440516,  0.14016749,
+        0.09184887,  0.16538898,  0.10497667,  0.20788886,  0.14531043,
+        0.05876227,  0.03538524,  0.02983533,  0.03374047,  0.04977869,
+        0.01542405,  0.03263745,  0.02259926,  0.05234669,  0.04206366,
+       -0.1844748 ,  0.01548927,  0.1646059 ,  0.26421102,  0.36827202,
+        0.53349795,  0.62817495,  0.68210746,  0.81952017,  0.97732828]), scaled_genome=None, log_likelihood=4173.352615864345, fitness_values=array([2.91164996, 2.00523629, 1.43568413, 1.43568413, 1.43568413,
+       1.43568413, 0.82807989, 0.82807989, 0.82807989, 0.82807989])), Solution(fitness=0.9738251717799502, genome=array([ 0.09169733,  0.19543627,  0.13790805,  0.20136339,  0.14125819,
+        0.09801727,  0.12802203,  0.19257582,  0.14867784,  0.15607876,
+        0.03841266,  0.02247159,  0.06152662,  0.03930066,  0.04529416,
+        0.05773186,  0.03414643,  0.02869377,  0.04483555,  0.0252011 ,
+       -0.22869894, -0.01336972,  0.08850327,  0.22472906,  0.31095184,
+        0.46748527,  0.58218535,  0.6862702 ,  0.87035786,  0.96850772]), scaled_genome=None, log_likelihood=3777.855660073808, fitness_values=array([2.01638984, 2.01638984, 1.72044581, 1.0672471 , 0.97382517,
+       0.97382517, 0.97382517, 0.97382517, 0.97382517, 0.97382517])), Solution(fitness=0.6422011368955184, genome=array([ 0.03      ,  0.25898133,  0.03976871,  0.16664105,  0.03      ,
+        0.0387397 ,  0.16299466,  0.29571994,  0.2123828 ,  0.04498073,
+        0.07253858,  0.01912492,  0.0160342 ,  0.04168855,  0.02285397,
+        0.01963015,  0.01006712,  0.04139807,  0.03767417,  0.03536959,
+       -0.12955337, -0.00843688,  0.06741238,  0.24564452,  0.4216597 ,
+        0.52552317,  0.56932692,  0.68655932,  0.88397997,  0.98993393]), scaled_genome=None, log_likelihood=3518.6984429895583, fitness_values=array([1.94981411, 1.94981411, 1.57307885, 1.50269205, 1.30379991,
+       0.70912639, 0.70912639, 0.64220114, 0.64220114, 0.64220114])), Solution(fitness=0.9342015524719666, genome=array([ 0.04026712,  0.22450875,  0.1093103 ,  0.15177775,  0.10649068,
+        0.03      ,  0.15187558,  0.19070102,  0.12434407,  0.18085991,
+        0.02432636,  0.04412493,  0.01755307,  0.03980986,  0.02975547,
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+        0.48166528,  0.61581601,  0.68989205,  0.8544333 ,  0.97089344]), scaled_genome=None, log_likelihood=2553.6945081733843, fitness_values=array([1.28627624, 1.28627624, 1.28627624, 1.28627624, 1.28627624,
+       1.28627624, 1.28627624, 1.14087666, 0.93420155, 0.93420155])), Solution(fitness=1.107540751862287, genome=array([ 0.03      ,  0.09241643,  0.1428768 ,  0.15393823,  0.15280382,
+        0.23917511,  0.20206637,  0.09533974,  0.07153465,  0.21778402,
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+       1.23060404, 1.23060404, 1.23060404, 1.10754075, 1.10754075]))]"
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+        8.89158706e-01,  9.79053536e-01]), scaled_genome=None, log_likelihood=5200.624908935032, fitness_values=array([0.98423088, 0.98423088, 0.70125287, 0.70125287, 0.5285313 ,
+       0.4925995 , 0.44944038, 0.37040652, 0.3147347 , 0.26177913])), Solution(fitness=0.257826242641395, genome=array([ 0.03      ,  0.21818679,  0.10747136,  0.0895118 ,  0.05969212,
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+       0.4500414 , 0.41341306, 0.35028196, 0.32512342, 0.25782624]))]"
+3,synthetic_10_72,GASolver,0.15349830312883742,0.043054548745849935,"[0.11805915 0.21758557 0.17531805 0.22419726 0.15921955 0.12559964
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+       0.18712906, 0.18345952, 0.13082487, 0.12578   , 0.11805915])), Solution(fitness=0.217585568979663, genome=array([ 3.64855812e-02,  2.91215527e-01,  5.17772476e-02,  7.68101127e-02,
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+        0.02652457,  0.02634906,  0.06801106,  0.01914468,  0.04280116,
+       -0.11928274,  0.00507533,  0.11221537,  0.23690851,  0.38901579,
+        0.52993509,  0.65747038,  0.81373776,  0.90889001,  0.9953887 ]), scaled_genome=None, log_likelihood=3702.6684294271527, fitness_values=array([0.51091589, 0.46338442, 0.34578479, 0.28942788, 0.25910256,
+       0.24563259, 0.23618092, 0.20297644, 0.19011392, 0.17531805])), Solution(fitness=0.2241972593635807, genome=array([ 0.06133314,  0.23440343,  0.05944245,  0.07278878,  0.05358751,
+        0.06497074,  0.21322477,  0.05674297,  0.12486037,  0.05864583,
+        0.09926317,  0.02489557,  0.05682919,  0.05036071,  0.05341203,
+        0.03627206,  0.02080655,  0.06000226,  0.02889726,  0.04027728,
+       -0.08772422, -0.00148622,  0.15122811,  0.26392493,  0.46185783,
+        0.58025926,  0.66434692,  0.77812182,  0.90614935,  1.00115215]), scaled_genome=None, log_likelihood=3507.8899562985825, fitness_values=array([0.5705276 , 0.4242854 , 0.4092922 , 0.36944756, 0.35348836,
+       0.29312712, 0.27753566, 0.2571973 , 0.24928207, 0.22419726])), Solution(fitness=0.1592195490003222, genome=array([ 0.02726508,  0.24893646,  0.05754835,  0.06811505,  0.03622573,
+        0.06661964,  0.2277282 ,  0.05549031,  0.15171461,  0.06035657,
+        0.09808167,  0.02057988,  0.06272302,  0.03016656,  0.03832221,
+        0.05130674,  0.02039313,  0.05267586,  0.03259739,  0.04468761,
+       -0.15522343, -0.00404315,  0.10320189,  0.22986564,  0.36750728,
+        0.49641606,  0.66196032,  0.7713596 ,  0.90458542,  1.01626955]), scaled_genome=None, log_likelihood=3773.8198706203, fitness_values=array([0.64894182, 0.31611809, 0.2998873 , 0.25855982, 0.22955581,
+       0.21228873, 0.20567477, 0.19284235, 0.16238207, 0.15921955])), Solution(fitness=0.1255996432286336, genome=array([ 3.87578646e-02,  2.49679678e-01,  3.92388616e-02,  5.81318331e-02,
+        3.99271480e-02,  4.67633447e-02,  2.60392286e-01,  5.23345701e-02,
+        1.18347622e-01,  9.64267923e-02,  8.98172116e-02,  2.20970370e-02,
+        4.66879969e-02,  2.53875797e-02,  8.04536970e-02,  4.16746564e-02,
+        2.19422367e-02,  5.25806005e-02,  3.31266685e-02,  7.66585278e-02,
+       -1.09240481e-01,  2.52979665e-04,  1.38061440e-01,  2.16986039e-01,
+        3.25579343e-01,  5.27182827e-01,  6.63676457e-01,  7.89411423e-01,
+        9.05004623e-01,  1.02146859e+00]), scaled_genome=None, log_likelihood=3830.0817437266455, fitness_values=array([0.58187654, 0.4110813 , 0.32822448, 0.21126834, 0.19912728,
+       0.15968622, 0.14356197, 0.13735641, 0.13145879, 0.12559964])), Solution(fitness=0.1308306096054051, genome=array([ 0.04381694,  0.23277405,  0.04917627,  0.08427534,  0.03444311,
+        0.04883875,  0.26713368,  0.05477519,  0.12535883,  0.05940784,
+        0.10700601,  0.01993742,  0.06169538,  0.02984932,  0.04781415,
+        0.04253994,  0.02453603,  0.05866828,  0.03271438,  0.03544427,
+       -0.1287678 , -0.00273821,  0.08522485,  0.22685492,  0.36700359,
+        0.51134857,  0.66486667,  0.79019759,  0.90415755,  0.99939505]), scaled_genome=None, log_likelihood=3920.669875672466, fitness_values=array([0.40998383, 0.24661903, 0.19017178, 0.17021285, 0.15928114,
+       0.14761112, 0.13465928, 0.13346052, 0.13234914, 0.13083061])), Solution(fitness=0.14164529993713726, genome=array([ 0.03885866,  0.26231808,  0.09678123,  0.0460229 ,  0.04712828,
+        0.04760009,  0.22527269,  0.06065014,  0.08882693,  0.086541  ,
+        0.12630495,  0.02208661,  0.05491357,  0.04164185,  0.04158319,
+        0.04361048,  0.02014356,  0.0689523 ,  0.02348824,  0.04751408,
+       -0.07467332,  0.00170973,  0.18853689,  0.36774685,  0.47275786,
+        0.56709215,  0.66570654,  0.78074584,  0.91074651,  0.97976812]), scaled_genome=None, log_likelihood=3942.068665727981, fitness_values=array([0.52599337, 0.43919792, 0.35275533, 0.25186265, 0.22523693,
+       0.20566019, 0.19545644, 0.16691226, 0.15761233, 0.1416453 ])), Solution(fitness=0.07567944739235549, genome=array([ 0.03860079,  0.26002419,  0.05405037,  0.05635047,  0.04857704,
+        0.03954905,  0.21927573,  0.08623711,  0.14369599,  0.05363926,
+        0.10728386,  0.01938068,  0.06572186,  0.02044026,  0.11142486,
+        0.03316788,  0.02001655,  0.06018738,  0.02702887,  0.03054212,
+       -0.10274275,  0.00250129,  0.11714761,  0.21530486,  0.41528902,
+        0.52949241,  0.65761405,  0.75151303,  0.914573  ,  1.00074776]), scaled_genome=None, log_likelihood=4263.0278861092975, fitness_values=array([0.89153808, 0.35695659, 0.2851415 , 0.22530518, 0.1699219 ,
+       0.14715094, 0.1400638 , 0.13151679, 0.09980994, 0.07567945])), Solution(fitness=0.16684845336082485, genome=array([ 3.24952381e-02,  2.51803595e-01,  4.97214127e-02,  6.26585223e-02,
+        3.72117734e-02,  6.94262667e-02,  2.45170994e-01,  4.58923354e-02,
+        1.09873379e-01,  9.57464838e-02,  1.09195470e-01,  2.33314276e-02,
+        5.17204171e-02,  2.98957063e-02,  3.87472861e-02,  3.61386017e-02,
+        2.17888085e-02,  3.59129010e-02,  3.88915340e-02,  4.50476635e-02,
+       -1.82871520e-01,  8.38167542e-04,  1.41509679e-01,  2.34109296e-01,
+        3.90734703e-01,  5.18775951e-01,  6.62059268e-01,  7.56775746e-01,
+        8.83816145e-01,  9.68016194e-01]), scaled_genome=None, log_likelihood=3739.1203970642414, fitness_values=array([0.54831139, 0.46565738, 0.38026571, 0.27547962, 0.25107251,
+       0.21984387, 0.19852989, 0.18414292, 0.17939229, 0.16684845]))]"
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+        3.28432183e-02,  5.83833117e-02,  2.34916330e-01,  7.03922876e-02,
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+        1.99510900e-02,  5.99517265e-02,  2.72190681e-02,  3.30800094e-02,
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+       0.55922888, 0.3821527 , 0.17784923, 0.08879471, 0.04170594])), Solution(fitness=0.059916091377177336, genome=array([ 0.03062216,  0.26956141,  0.03842917,  0.08162416,  0.03661341,
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+       0.6075638 , 0.48319975, 0.28407028, 0.12926965, 0.04626602])), Solution(fitness=0.057398083118777386, genome=array([ 3.06146186e-02,  2.51913135e-01,  4.37283940e-02,  7.34348425e-02,
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+        9.10159389e-01,  9.83895642e-01]), scaled_genome=None, log_likelihood=5169.205819117459, fitness_values=array([2.2227284 , 1.00274578, 1.00274578, 0.92392209, 0.49620311,
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+       0.53180529, 0.4214457 , 0.29494963, 0.13421911, 0.06994399])), Solution(fitness=0.036947359816103094, genome=array([ 3.56177880e-02,  2.69120481e-01,  4.24538701e-02,  6.51771435e-02,
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+       -1.04946331e-01,  9.42151107e-04,  9.90119844e-02,  2.06142162e-01,
+        3.64238258e-01,  5.27166163e-01,  6.62817372e-01,  7.70919555e-01,
+        9.12213763e-01,  9.90729076e-01]), scaled_genome=None, log_likelihood=5017.735359028502, fitness_values=array([1.39532367, 1.06241778, 1.06241778, 0.77203322, 0.53162065,
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+       0.50590404, 0.37468535, 0.2525451 , 0.13255404, 0.04938495])), Solution(fitness=0.05396810147466594, genome=array([ 0.03037736,  0.28709025,  0.03694721,  0.06270752,  0.03330024,
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+       0.50908921, 0.44609734, 0.33277482, 0.10510427, 0.0539681 ]))]"
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+       0.25757941, 0.1946032 , 0.13141344, 0.08505414, 0.05546154])), Solution(fitness=0.1062779140602888, genome=array([2.58873586e-01, 2.54641248e-01, 3.06184654e-02, 1.18307474e-01,
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+       5.34429445e-02, 6.94150420e-02, 3.90029088e-02, 1.69598257e-01,
+       4.13841663e-02, 3.06517071e-02, 1.12907337e-02, 3.39402578e-02,
+       6.71975616e-02, 1.26264568e-02, 2.39039777e-02, 1.50010448e-02,
+       3.14755452e-02, 1.50654070e-02, 4.48469456e-02, 3.25511978e-02,
+       1.39707841e-05, 9.57720916e-02, 2.12665089e-01, 3.71264598e-01,
+       4.19615545e-01, 4.89231064e-01, 6.74521937e-01, 7.38981904e-01,
+       8.04911191e-01, 9.20967014e-01]), scaled_genome=None, log_likelihood=8075.5316551031065, fitness_values=array([3.01952797, 1.20449326, 0.89003573, 0.54604864, 0.51569975,
+       0.31672972, 0.27298653, 0.21997711, 0.09801061, 0.06676292])), Solution(fitness=0.10258413278669887, genome=array([2.61898396e-01, 2.49939136e-01, 3.16088823e-02, 9.17443517e-02,
+       1.18801457e-01, 3.00523487e-02, 3.54023162e-02, 1.85889844e-01,
+       3.24943986e-02, 3.02609779e-02, 1.07967946e-02, 3.69579006e-02,
+       4.07361338e-02, 1.54326845e-02, 3.24522341e-02, 2.43566835e-02,
+       1.45621889e-02, 1.41105517e-02, 2.52576386e-02, 3.61404985e-02,
+       1.82946312e-04, 9.74782707e-02, 2.53408632e-01, 3.75629685e-01,
+       4.74135610e-01, 6.61877946e-01, 7.11825872e-01, 7.41475130e-01,
+       8.22753872e-01, 9.11043561e-01]), scaled_genome=None, log_likelihood=8131.7321666573425, fitness_values=array([1.61848558, 1.47264177, 0.95070243, 0.6910317 , 0.45867368,
+       0.25405157, 0.21004458, 0.17299093, 0.1310973 , 0.10258413])), Solution(fitness=0.06348218866295621, genome=array([2.89426850e-01, 2.65755875e-01, 3.25486642e-02, 8.00535289e-02,
+       5.49142667e-02, 7.48779693e-02, 3.40210541e-02, 1.94804679e-01,
+       5.13459476e-02, 3.00000000e-02, 1.11655683e-02, 3.72265305e-02,
+       5.74741385e-02, 1.10164521e-02, 2.97108940e-02, 1.12736081e-02,
+       2.28420644e-02, 1.58552999e-02, 6.02896435e-02, 6.20508084e-02,
+       3.55858014e-04, 9.25727057e-02, 2.44050793e-01, 3.72116633e-01,
+       4.32981663e-01, 4.91652011e-01, 6.63739735e-01, 7.37740009e-01,
+       8.17611549e-01, 1.03331746e+00]), scaled_genome=None, log_likelihood=8593.036550855348, fitness_values=array([3.20079379, 2.01696686, 1.32114111, 0.7109456 , 0.5070524 ,
+       0.3012838 , 0.2401837 , 0.15584174, 0.09388885, 0.06348219])), Solution(fitness=0.10828134201485028, genome=array([2.72545903e-01, 2.70519886e-01, 3.04731013e-02, 1.17501020e-01,
+       9.61471668e-02, 3.01794606e-02, 3.34729033e-02, 1.91764465e-01,
+       3.84534377e-02, 3.02342440e-02, 1.09953304e-02, 3.91631678e-02,
+       1.71927215e-02, 2.31473341e-02, 2.28874511e-02, 1.78331668e-02,
+       2.13160943e-02, 1.51253529e-02, 4.85942604e-02, 1.61876446e-02,
+       2.01408688e-04, 9.44532743e-02, 2.41393186e-01, 3.78844383e-01,
+       4.82005946e-01, 6.41198067e-01, 6.99720937e-01, 7.40690179e-01,
+       8.51323821e-01, 1.03294595e+00]), scaled_genome=None, log_likelihood=8246.940228905152, fitness_values=array([2.23157649, 2.01400595, 1.08707611, 0.65359949, 0.54009312,
+       0.3029873 , 0.25022805, 0.1738844 , 0.1357311 , 0.10828134])), Solution(fitness=0.09818647184519971, genome=array([0.28156164, 0.25734515, 0.03097877, 0.11573831, 0.06112768,
+       0.04255639, 0.03936654, 0.19578687, 0.03311258, 0.03083954,
+       0.01106439, 0.0390311 , 0.0282163 , 0.021677  , 0.02247896,
+       0.01150905, 0.04084946, 0.0170661 , 0.03412858, 0.04179415,
+       0.00104569, 0.09904182, 0.24410405, 0.37493129, 0.46720937,
+       0.49942128, 0.67677057, 0.7390291 , 0.82747416, 0.91309644]), scaled_genome=None, log_likelihood=8387.793616446716, fitness_values=array([3.12380415, 1.48828618, 0.8521824 , 0.4440847 , 0.43053709,
+       0.28962145, 0.21985455, 0.1603745 , 0.12161878, 0.09818647])), Solution(fitness=0.11125188919735338, genome=array([2.69474989e-01, 2.64808146e-01, 3.04145122e-02, 1.15217067e-01,
+       1.03492009e-01, 3.10033711e-02, 3.36086918e-02, 1.84190299e-01,
+       3.71164813e-02, 3.18639944e-02, 1.09920822e-02, 3.78143133e-02,
+       2.31740755e-02, 2.23516507e-02, 2.31807019e-02, 1.30534458e-02,
+       2.06921729e-02, 1.46269223e-02, 3.85299838e-02, 2.39347431e-02,
+       1.94375751e-05, 9.66786794e-02, 2.47214235e-01, 3.81836357e-01,
+       4.78905675e-01, 6.51667178e-01, 6.96526252e-01, 7.40124216e-01,
+       8.04684078e-01, 9.11943126e-01]), scaled_genome=None, log_likelihood=8256.533920287102, fitness_values=array([2.30500478, 1.97544042, 1.23859393, 0.82658271, 0.58553409,
+       0.40198339, 0.29433938, 0.19089478, 0.14238787, 0.11125189])), Solution(fitness=0.0699028518413623, genome=array([2.75087177e-01, 2.57321548e-01, 3.06868169e-02, 6.97075449e-02,
+       6.42090044e-02, 6.01663745e-02, 4.40305686e-02, 1.70972544e-01,
+       3.75122344e-02, 3.03395677e-02, 1.12918289e-02, 3.71522973e-02,
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+       4.57566555e-02, 1.47388613e-02, 4.80476949e-02, 2.01406541e-02,
+       3.56972687e-04, 9.81135931e-02, 2.47046516e-01, 3.70582575e-01,
+       4.25690953e-01, 4.89781478e-01, 6.92841548e-01, 7.39054996e-01,
+       8.29412087e-01, 9.34690017e-01]), scaled_genome=None, log_likelihood=8037.936227297568, fitness_values=array([1.49842553, 1.49842553, 1.00160994, 0.69253019, 0.49807085,
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+       1.17759594e-01, 3.12271904e-02, 4.29579751e-02, 1.84621267e-01,
+       3.00000000e-02, 3.30156273e-02, 1.11738667e-02, 3.92550004e-02,
+       3.55548290e-02, 1.41264657e-02, 3.37028483e-02, 6.57207735e-02,
+       2.39325985e-02, 1.46020057e-02, 3.64972226e-02, 3.15186676e-02,
+       4.41353230e-04, 9.24852037e-02, 2.53009077e-01, 3.73405652e-01,
+       4.69428224e-01, 6.09345622e-01, 7.02341642e-01, 7.40224475e-01,
+       8.03091854e-01, 8.99249572e-01]), scaled_genome=None, log_likelihood=8364.122118791638, fitness_values=array([2.73170241, 1.93852974, 1.03374849, 0.56583206, 0.47876113,
+       0.33357968, 0.23154638, 0.17134922, 0.13129456, 0.10875989]))]"
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+       1.02981142, 1.02981142, 1.02981142, 1.02981142, 0.865073  ])), Solution(fitness=1.0838074000550542, genome=array([0.44415935, 0.12342937, 0.11595156, 0.11610443, 0.05151712,
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+       1.51504757, 1.3761887 , 1.17144491, 1.0838074 , 1.0838074 ])), Solution(fitness=0.6353027774591123, genome=array([0.18121869, 0.1812027 , 0.07906131, 0.16754575, 0.11988327,
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+       -0.0028205 ,  0.07782278,  0.28211693,  0.36558957,  0.45556239,
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+       1.14150309, 1.14150309, 1.05430752, 1.05430752, 1.05430752])), Solution(fitness=1.031288677590322, genome=array([0.18864541, 0.16750709, 0.14368358, 0.22941702, 0.04626916,
+       0.1691929 , 0.15616506, 0.11523582, 0.132936  , 0.06041455,
+       0.02226707, 0.02124529, 0.03455624, 0.03024719, 0.02731494,
+       0.02850092, 0.06167102, 0.01803087, 0.04641668, 0.03591258,
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+        0.05097442,  0.19567298,  0.15942321,  0.03      ,  0.13000923,
+        0.0339777 ,  0.03152262,  0.01499465,  0.03970168,  0.03657652,
+        0.05063055,  0.02488391,  0.01920452,  0.03672605,  0.02060794,
+       -0.00466464,  0.09384109,  0.2640186 ,  0.35100136,  0.45651372,
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+       1.17708131, 1.17708131, 1.11785463, 1.11785463, 1.06922495])), Solution(fitness=1.2198083231190142, genome=array([0.2973619 , 0.05503611, 0.09515958, 0.18826069, 0.05418608,
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+       0.01386168, 0.04297939, 0.0221856 , 0.01243087, 0.01550846,
+       0.0261115 , 0.15896421, 0.25984207, 0.34932903, 0.45991329,
+       0.5021498 , 0.65434219, 0.75248994, 0.81718433, 0.91336325]), scaled_genome=None, log_likelihood=5243.496006498201, fitness_values=array([2.64825446, 2.60193993, 2.43901143, 2.04041368, 1.87045103,
+       1.52064256, 1.52064256, 1.52064256, 1.21980832, 1.21980832]))]"
+2,synthetic_10_99,SHADESolver,0.3497649820138224,0.06863817503551708,"[0.42122201 0.45355414 0.33440367 0.46538833 0.33087633 0.2594019
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+       0.74365857, 0.62238025, 0.50252641, 0.47286056, 0.42122201])), Solution(fitness=0.4535541434393491, genome=array([0.43492158, 0.14621975, 0.03      , 0.11176065, 0.14175987,
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+       0.66932703, 0.52432884, 0.4883063 , 0.37634961, 0.33440367])), Solution(fitness=0.46538833487250303, genome=array([0.36097123, 0.1419522 , 0.03172812, 0.13089699, 0.10172594,
+       0.03746008, 0.03749784, 0.16494339, 0.05023272, 0.04788545,
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+       0.71403098, 0.62139535, 0.56617747, 0.52289757, 0.46538833])), Solution(fitness=0.33087632549274276, genome=array([0.25760029, 0.22524559, 0.06263323, 0.12241379, 0.10164643,
+       0.05896745, 0.05390041, 0.15535548, 0.06429575, 0.05080832,
+       0.01752684, 0.03689107, 0.03074052, 0.01990245, 0.02743233,
+       0.01592519, 0.02255628, 0.02081847, 0.03662387, 0.02392617,
+       0.00660211, 0.08982004, 0.26623009, 0.37745379, 0.44559251,
+       0.49482795, 0.68129989, 0.74160188, 0.82460261, 0.9545193 ]), scaled_genome=None, log_likelihood=7491.146715946633, fitness_values=array([2.47066619, 1.89997445, 1.42788162, 1.05836395, 0.87886559,
+       0.74316351, 0.64950844, 0.5352864 , 0.40624095, 0.33087633])), Solution(fitness=0.25940190171762706, genome=array([0.27384109, 0.20404994, 0.03      , 0.0947855 , 0.13050165,
+       0.03177716, 0.07597773, 0.14786613, 0.03      , 0.03      ,
+       0.0118884 , 0.03977292, 0.02885764, 0.0185934 , 0.03407323,
+       0.02633167, 0.03096816, 0.02218177, 0.04035327, 0.02123984,
+       0.00222055, 0.09994889, 0.24624695, 0.36477668, 0.46681875,
+       0.54114636, 0.64801795, 0.74129057, 0.83942627, 0.93892792]), scaled_genome=None, log_likelihood=6990.230642342735, fitness_values=array([1.95866735, 1.0632425 , 1.0632425 , 0.7808433 , 0.7808433 ,
+       0.6363445 , 0.55376055, 0.38250793, 0.32530232, 0.2594019 ])), Solution(fitness=0.3410626697127436, genome=array([0.30798514, 0.18467187, 0.0325855 , 0.12049005, 0.1374261 ,
+       0.05873052, 0.06172582, 0.17011569, 0.07762214, 0.03588543,
+       0.01269803, 0.03093052, 0.02607239, 0.02431121, 0.03623935,
+       0.02506109, 0.03511766, 0.01662135, 0.01879692, 0.04475652,
+       0.00475586, 0.09670796, 0.22869687, 0.36164427, 0.44032454,
+       0.52552353, 0.66361707, 0.74494466, 0.84744522, 0.93546572]), scaled_genome=None, log_likelihood=7788.754762886576, fitness_values=array([2.50569223, 1.53544146, 1.25014156, 0.99190675, 0.81581056,
+       0.67151056, 0.59523563, 0.53848903, 0.40465046, 0.34106267])), Solution(fitness=0.2826696645803386, genome=array([ 3.26295608e-01,  2.67953275e-01,  3.50782107e-02,  1.19669115e-01,
+        9.36400891e-02,  5.41847652e-02,  8.61991643e-02,  1.90790947e-01,
+        7.89388660e-02,  4.44903487e-02,  1.08739508e-02,  3.17593602e-02,
+        2.91068292e-02,  1.59451268e-02,  2.40530400e-02,  1.82837505e-02,
+        2.30628080e-02,  1.96668845e-02,  2.76204885e-02,  5.02202673e-02,
+       -7.36616636e-04,  9.78431974e-02,  2.59910231e-01,  3.77015589e-01,
+        4.74737129e-01,  5.56677033e-01,  6.51798304e-01,  7.40145017e-01,
+        8.36394054e-01,  9.31137289e-01]), scaled_genome=None, log_likelihood=9067.531813465679, fitness_values=array([2.33817241, 1.19067479, 0.98839178, 0.83244101, 0.60730153,
+       0.43900602, 0.39637997, 0.37339688, 0.30465831, 0.28266966])), Solution(fitness=0.2897405871955548, genome=array([3.32575365e-01, 2.05984120e-01, 3.00000000e-02, 1.52810740e-01,
+       9.46222376e-02, 9.31751432e-02, 8.39486013e-02, 2.05825965e-01,
+       9.16689605e-02, 3.65721641e-02, 1.20707361e-02, 3.08952848e-02,
+       3.38382212e-02, 1.34159465e-02, 3.48491029e-02, 1.33643468e-02,
+       3.31013543e-02, 1.59780092e-02, 3.48383425e-02, 2.26868843e-02,
+       8.89427812e-04, 9.45461962e-02, 2.58308831e-01, 3.69166478e-01,
+       4.56275783e-01, 5.08618225e-01, 6.61762369e-01, 7.44582845e-01,
+       8.30264794e-01, 9.50614325e-01]), scaled_genome=None, log_likelihood=9456.155663268946, fitness_values=array([1.90198331, 1.61291624, 1.19820052, 0.90347163, 0.7076858 ,
+       0.64613898, 0.52226438, 0.39538312, 0.34796464, 0.28974059])), Solution(fitness=0.3193305136670203, genome=array([0.28020425, 0.18329961, 0.03      , 0.12964042, 0.08611725,
+       0.08402302, 0.04968001, 0.18020264, 0.0575788 , 0.03      ,
+       0.0172037 , 0.03795646, 0.02376758, 0.01605342, 0.0249821 ,
+       0.03707648, 0.01727871, 0.02380623, 0.03836198, 0.01674633,
+       0.00290175, 0.10174363, 0.22253376, 0.36828852, 0.4382677 ,
+       0.51223313, 0.66394886, 0.73631477, 0.8150253 , 0.92437858]), scaled_genome=None, log_likelihood=7123.691435118981, fitness_values=array([2.21916055, 1.31151504, 1.09634981, 0.96987064, 0.7884392 ,
+       0.68201708, 0.59860849, 0.47451929, 0.39870169, 0.31933051]))]"
+3,synthetic_10_99,GASolver,0.3124855653648665,0.12430560887536217,"[0.15564756 0.3241978  0.51201523 0.17460591 0.2868831  0.24000601
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+       0.01228069, 0.0374436 , 0.02975331, 0.07838445, 0.02170379,
+       0.02251456, 0.07467239, 0.01900888, 0.04374242, 0.08139739,
+       0.00125949, 0.06145269, 0.13404012, 0.26013426, 0.37950603,
+       0.48101595, 0.6622865 , 0.74139095, 0.8395243 , 0.9544236 ]), scaled_genome=None, log_likelihood=7165.423043539098, fitness_values=array([0.87195979, 0.60674283, 0.48939833, 0.34686997, 0.32543989,
+       0.24057505, 0.21660053, 0.17497472, 0.16423728, 0.15564756])), Solution(fitness=0.32419780448908736, genome=array([0.32294743, 0.18221392, 0.02602287, 0.04568978, 0.06816659,
+       0.08500597, 0.03922229, 0.16771513, 0.03230344, 0.03071259,
+       0.01980485, 0.03199202, 0.02516095, 0.03661602, 0.02921296,
+       0.02464355, 0.04348943, 0.01644593, 0.03213827, 0.07388372,
+       0.00963175, 0.10303084, 0.23096438, 0.32060503, 0.392323  ,
+       0.48619349, 0.64726446, 0.73958353, 0.80379149, 0.9323191 ]), scaled_genome=None, log_likelihood=6276.567306792355, fitness_values=array([1.00024405, 0.61468818, 0.52120373, 0.45953393, 0.41869896,
+       0.36096747, 0.35225883, 0.34455706, 0.32787973, 0.3241978 ])), Solution(fitness=0.5120152254348667, genome=array([0.5266264 , 0.0536453 , 0.0202894 , 0.07197314, 0.06970305,
+       0.02388945, 0.03034029, 0.15318793, 0.02627859, 0.02406645,
+       0.04922654, 0.02888591, 0.04209579, 0.01784458, 0.02785292,
+       0.06306882, 0.02538883, 0.02244934, 0.04063469, 0.09591748,
+       0.03609588, 0.15430516, 0.27315484, 0.37774158, 0.46751424,
+       0.57626786, 0.6741649 , 0.73795622, 0.87211018, 0.98401545]), scaled_genome=None, log_likelihood=5198.041035051944, fitness_values=array([0.9980176 , 0.68178854, 0.60587045, 0.58871537, 0.53778029,
+       0.52960549, 0.52785637, 0.52765207, 0.52385311, 0.51201523])), Solution(fitness=0.1746059087868524, genome=array([2.26683737e-01, 2.15654552e-01, 3.82351626e-02, 8.29480566e-02,
+       1.04264254e-01, 3.51383639e-02, 5.13278345e-02, 1.72810606e-01,
+       3.28358423e-02, 4.01015903e-02, 1.19759748e-02, 4.39018725e-02,
+       8.19346832e-02, 1.46855531e-02, 3.23791149e-02, 4.48387020e-02,
+       3.55116304e-02, 1.74230523e-02, 5.08503184e-02, 8.71963840e-02,
+       6.87895124e-04, 9.91361059e-02, 2.74247351e-01, 3.75346651e-01,
+       4.60850744e-01, 5.55896136e-01, 6.68923377e-01, 7.38248115e-01,
+       8.27453473e-01, 9.80537048e-01]), scaled_genome=None, log_likelihood=6965.689636772581, fitness_values=array([0.72769873, 0.4747285 , 0.29505347, 0.25800285, 0.22929409,
+       0.21440268, 0.19543495, 0.18905175, 0.17689067, 0.17460591])), Solution(fitness=0.28688309886935653, genome=array([0.25071366, 0.18723622, 0.05418879, 0.05251724, 0.09698013,
+       0.06238304, 0.04561447, 0.14851447, 0.07218117, 0.0296708 ,
+       0.01489659, 0.03113901, 0.06148096, 0.03081989, 0.03547737,
+       0.01659297, 0.05542038, 0.01973148, 0.05062606, 0.09581044,
+       0.00547166, 0.0945802 , 0.19607415, 0.33356689, 0.4116049 ,
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+       0.39520688, 0.33831763, 0.30685873, 0.29362109, 0.2868831 ])), Solution(fitness=0.24000601330509558, genome=array([0.24862925, 0.21544821, 0.04098975, 0.02457093, 0.09030905,
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+       0.01328668, 0.0348874 , 0.03223445, 0.0321814 , 0.02968846,
+       0.0259492 , 0.06833475, 0.02627252, 0.03653814, 0.08156773,
+       0.00230684, 0.09971096, 0.2013947 , 0.30047145, 0.38026655,
+       0.47740123, 0.58826555, 0.73951404, 0.85302877, 0.97303083]), scaled_genome=None, log_likelihood=6688.56161996141, fitness_values=array([1.12207612, 0.79645616, 0.56926082, 0.44006749, 0.37386779,
+       0.33597125, 0.31713844, 0.29705791, 0.25604972, 0.24000601])), Solution(fitness=0.16853628285975153, genome=array([0.29312055, 0.14116628, 0.07835973, 0.02890635, 0.11369342,
+       0.06725208, 0.0358023 , 0.16737594, 0.03618226, 0.03814109,
+       0.0135493 , 0.01720322, 0.01531167, 0.03688855, 0.02831113,
+       0.01995851, 0.0577882 , 0.01900986, 0.04112359, 0.0888646 ,
+       0.00639056, 0.07917658, 0.1403677 , 0.24206376, 0.3810375 ,
+       0.48575703, 0.64034471, 0.73190477, 0.82427717, 0.96645735]), scaled_genome=None, log_likelihood=7059.200043393067, fitness_values=array([0.77795183, 0.51183587, 0.38966056, 0.32592406, 0.27932316,
+       0.23562593, 0.20650035, 0.19657779, 0.17792162, 0.16853628])), Solution(fitness=0.495449370772132, genome=array([0.49618833, 0.0457597 , 0.03442298, 0.06379123, 0.05751232,
+       0.0452656 , 0.04252923, 0.14813027, 0.0350555 , 0.03134484,
+       0.0437    , 0.01150381, 0.06413957, 0.02938214, 0.03945397,
+       0.01958463, 0.04253507, 0.01557228, 0.04719288, 0.09612787,
+       0.0312682 , 0.13555088, 0.26014251, 0.3486067 , 0.43124338,
+       0.50529379, 0.66848174, 0.73837529, 0.81508361, 0.93055391]), scaled_genome=None, log_likelihood=5241.583426268649, fitness_values=array([0.71449301, 0.65175165, 0.61636379, 0.57972771, 0.55069295,
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+       0.08167924, 0.02558343, 0.16671316, 0.03199087, 0.03183629,
+       0.02735776, 0.02944894, 0.03868405, 0.03691854, 0.02216061,
+       0.02355266, 0.03683248, 0.01956381, 0.03501187, 0.08881823,
+       0.01626009, 0.1071944 , 0.20073096, 0.29969706, 0.38355619,
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+       0.432685  , 0.42177711, 0.40746903, 0.38313987, 0.36511033])), Solution(fitness=0.4024040601234741, genome=array([0.45431063, 0.08221942, 0.024195  , 0.07535042, 0.07260482,
+       0.02727515, 0.03292913, 0.16345488, 0.03925017, 0.02841038,
+       0.03971953, 0.02113557, 0.08059646, 0.01490165, 0.02988156,
+       0.02709315, 0.02756755, 0.01861452, 0.04959544, 0.05169966,
+       0.02324516, 0.13152216, 0.22851366, 0.3707989 , 0.45632789,
+       0.51927329, 0.65259329, 0.73908104, 0.81219836, 0.94197594]), scaled_genome=None, log_likelihood=5691.383202542978, fitness_values=array([0.76210573, 0.67027304, 0.56068482, 0.51068317, 0.48378905,
+       0.47454791, 0.46358516, 0.45084526, 0.40522669, 0.40240406]))]"
+4,synthetic_10_99,LSHADESolver,0.10065383772316297,0.018615493700380945,"[0.09407012 0.08185595 0.11845917 0.09698181 0.07517484 0.08365581
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+        9.12027506e-02,  5.19212786e-02,  4.13056514e-02,  2.03595580e-01,
+        3.75863950e-02,  3.15378185e-02,  1.13675865e-02,  3.86429168e-02,
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+        2.92753780e-02,  1.70521077e-02,  3.94910534e-02,  2.35942866e-02,
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+        4.48984481e-01,  4.96116217e-01,  6.77997319e-01,  7.39827457e-01,
+        8.37355493e-01,  9.27880641e-01]), scaled_genome=None, log_likelihood=8264.853536649782, fitness_values=array([3.28971479, 1.46204577, 1.31375401, 1.31375401, 1.241059  ,
+       0.77916472, 0.63782868, 0.34313635, 0.18787037, 0.09407012])), Solution(fitness=0.0818559473901753, genome=array([2.82646047e-01, 2.65733318e-01, 3.07724454e-02, 8.23452666e-02,
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+       4.26613799e-02, 1.49638871e-02, 5.03632674e-02, 1.78423104e-02,
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+       8.48067378e-01, 9.93449220e-01]), scaled_genome=None, log_likelihood=8347.258323133587, fitness_values=array([3.75264863, 2.11216071, 1.34057654, 1.15152748, 1.1066676 ,
+       0.72326264, 0.53998043, 0.39312106, 0.17749934, 0.08185595])), Solution(fitness=0.11845917498074052, genome=array([ 2.90825600e-01,  2.63770283e-01,  3.08215617e-02,  1.23884247e-01,
+        9.70335089e-02,  3.11467663e-02,  3.00401226e-02,  1.92728245e-01,
+        3.22742484e-02,  3.07135077e-02,  1.22196996e-02,  3.74264257e-02,
+        4.27014787e-02,  2.04057580e-02,  2.59205848e-02,  4.22392103e-02,
+        2.19268607e-02,  1.49307073e-02,  3.16546416e-02,  5.19817618e-02,
+       -6.83861156e-04,  9.33331672e-02,  2.25833426e-01,  3.78452548e-01,
+        4.76140726e-01,  5.72535774e-01,  6.73915377e-01,  7.39027712e-01,
+        7.94165300e-01,  9.06081416e-01]), scaled_genome=None, log_likelihood=8538.50116128025, fitness_values=array([3.61556096, 2.13235693, 1.86510537, 1.46178367, 1.07424989,
+       0.75650685, 0.38635993, 0.31315176, 0.21577589, 0.11845917])), Solution(fitness=0.09698181183915718, genome=array([2.78439667e-01, 2.58663593e-01, 3.14199411e-02, 1.12517141e-01,
+       8.02078678e-02, 3.23667207e-02, 4.24061129e-02, 1.99598847e-01,
+       4.10782141e-02, 3.36206609e-02, 1.12943384e-02, 3.67695995e-02,
+       4.34053217e-02, 1.77490282e-02, 2.39657270e-02, 8.28054934e-03,
+       1.93829010e-02, 1.58528539e-02, 3.70701776e-02, 2.84609953e-02,
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+       4.64291089e-01, 4.99282593e-01, 6.67713332e-01, 7.38444689e-01,
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+       0.67992686, 0.44787677, 0.27143004, 0.1667158 , 0.09698181])), Solution(fitness=0.07517483799575023, genome=array([2.62181985e-01, 2.46612054e-01, 3.10507241e-02, 9.02165979e-02,
+       6.51862336e-02, 5.76238410e-02, 3.11796388e-02, 2.12089988e-01,
+       3.01295645e-02, 3.52007176e-02, 1.08856698e-02, 3.75583924e-02,
+       2.45024809e-02, 1.37108504e-02, 3.18077435e-02, 9.83212420e-03,
+       3.29351578e-02, 1.72755525e-02, 2.86547410e-02, 2.87004135e-02,
+       4.66315943e-04, 9.50885163e-02, 2.48364075e-01, 3.69947091e-01,
+       4.39554627e-01, 4.91324073e-01, 6.62594347e-01, 7.38189843e-01,
+       8.20714845e-01, 9.26318676e-01]), scaled_genome=None, log_likelihood=8241.72235359023, fitness_values=array([3.1063757 , 1.74510118, 1.51850824, 1.08920704, 0.80704283,
+       0.62653803, 0.39758602, 0.26397963, 0.16662325, 0.07517484])), Solution(fitness=0.08365580762396523, genome=array([2.69876511e-01, 2.61043016e-01, 3.75696863e-02, 9.46153710e-02,
+       6.27303441e-02, 6.22461580e-02, 4.09593760e-02, 2.01419430e-01,
+       3.55283817e-02, 3.05270136e-02, 1.08453169e-02, 3.83607402e-02,
+       3.66043912e-02, 1.61708308e-02, 3.06896688e-02, 1.17758738e-02,
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+       4.32175301e-04, 9.26783187e-02, 2.47036243e-01, 3.71156443e-01,
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+       8.32516626e-01, 9.26714436e-01]), scaled_genome=None, log_likelihood=8269.445414291176, fitness_values=array([3.58300828, 1.96592601, 1.74734707, 1.33642034, 0.82201511,
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+       0.33426485, 0.2654781 , 0.2654781 , 0.25454343, 0.2438777 ]))]"
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+        0.03586783,  0.02943916,  0.01490113,  0.013563  ,  0.02325523,
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+       0.03304148, 0.03812914, 0.02852096, 0.029693  , 0.03756629,
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+       0.03281584, 0.1245455 , 0.20907235, 0.32185363, 0.36089793,
+       0.50016989, 0.5501995 , 0.62924552, 0.70232524, 0.77138816]), scaled_genome=None, log_likelihood=3604.293146938845, fitness_values=array([4.59934167, 3.95507429, 3.15252928, 1.72533343, 1.72533343,
+       1.72533343, 1.72533343, 1.58870451, 1.3881006 , 1.3881006 ])), Solution(fitness=1.019714392741707, genome=array([0.27782901, 0.16080039, 0.03817834, 0.23091218, 0.03      ,
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+       0.47319568, 0.54188962, 0.64432138, 0.72060138, 0.80348335]), scaled_genome=None, log_likelihood=6921.440591673969, fitness_values=array([5.33960529, 3.0143503 , 2.04211958, 1.9079359 , 1.82108443,
+       1.74213981, 1.38606261, 1.38606261, 1.01971439, 1.01971439])), Solution(fitness=1.0583079255927688, genome=array([0.13371471, 0.1072428 , 0.11784668, 0.14457952, 0.04210887,
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+       0.04462206, 0.02062039, 0.03496884, 0.01987068, 0.02743887,
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+       0.46439745, 0.53125673, 0.66610611, 0.72975829, 0.79295122]), scaled_genome=None, log_likelihood=4939.578772807476, fitness_values=array([2.54449981, 2.54449981, 2.54449981, 1.61569321, 1.61569321,
+       1.54605984, 1.24543646, 1.05830793, 1.05830793, 1.05830793])), Solution(fitness=1.0370842119079962, genome=array([0.19201647, 0.15438465, 0.04420745, 0.21513963, 0.12360481,
+       0.10903029, 0.03      , 0.03312425, 0.08090976, 0.18255255,
+       0.03279573, 0.02496245, 0.02968468, 0.02692885, 0.0184054 ,
+       0.01737083, 0.01234603, 0.01762423, 0.02409503, 0.0119514 ,
+       0.03281584, 0.15519005, 0.23766719, 0.33810769, 0.41548111,
+       0.50491266, 0.56030629, 0.62895112, 0.73922616, 0.79821235]), scaled_genome=None, log_likelihood=5144.374137088779, fitness_values=array([3.4271912 , 3.42459361, 3.42459361, 1.99085993, 1.77567517,
+       1.77567517, 1.40214203, 1.40146954, 1.40146954, 1.03708421])), Solution(fitness=1.178051395150181, genome=array([0.05405865, 0.28492769, 0.13502429, 0.23176093, 0.10695382,
+       0.03497366, 0.10382606, 0.19554046, 0.13188255, 0.18467757,
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+       1.29999759, 1.29999759, 1.29999759, 1.29999759, 1.1780514 ])), Solution(fitness=1.22588858447152, genome=array([0.18673658, 0.27812574, 0.04405313, 0.15447266, 0.03      ,
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+       2.0316763 , 1.81990197, 1.30568329, 1.30568329, 1.22588858])), Solution(fitness=1.0525214622943857, genome=array([0.19666254, 0.14697225, 0.11627614, 0.2013176 , 0.08703914,
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+       1.74038368, 1.48364671, 1.48364671, 1.44170141, 1.05252146])), Solution(fitness=1.4528053037168323, genome=array([0.1789809 , 0.1261195 , 0.06585391, 0.11265982, 0.03443017,
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+       2.0947726 , 2.0947726 , 1.61190088, 1.4528053 , 1.4528053 ])), Solution(fitness=1.3569352612762475, genome=array([0.19366104, 0.15597585, 0.21433222, 0.2168966 , 0.2735108 ,
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+       0.4510274 , 0.51373004, 0.65447292, 0.71888551, 0.79322293]), scaled_genome=None, log_likelihood=7211.456117333272, fitness_values=array([5.66088464, 3.68639726, 1.82087364, 1.82087364, 1.82087364,
+       1.82087364, 1.66017351, 1.61396816, 1.35693526, 1.35693526])), Solution(fitness=1.2628568500079602, genome=array([0.16669307, 0.18929174, 0.03      , 0.19504356, 0.03      ,
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+       0.4636326 , 0.51530142, 0.60547788, 0.69181684, 0.781645  ]), scaled_genome=None, log_likelihood=5142.892067325298, fitness_values=array([5.51384142, 3.16545199, 3.16545199, 2.68955871, 2.04074698,
+       1.85656835, 1.83204477, 1.26285685, 1.26285685, 1.26285685]))]"
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+       0.61241703, 0.53131391, 0.4494694 , 0.37975286, 0.36915259]))]"
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+       0.3573818 , 0.30147012, 0.27129214, 0.24185131, 0.23345021])), Solution(fitness=0.27492371706778634, genome=array([ 0.04718543,  0.05102368,  0.10302594,  0.07670911,  0.47558229,
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+        0.10655185,  0.01336615,  0.01960626,  0.07070896,  0.03159633,
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+       0.34151395, 0.33108481, 0.2955948 , 0.2910332 , 0.27492372])), Solution(fitness=0.2959860181785482, genome=array([ 0.02996885,  0.06511341,  0.09434689,  0.07350626,  0.48670524,
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+       0.02441373, 0.03370769, 0.02237669, 0.03490568, 0.01687221,
+       0.09635454, 0.12618482, 0.204448  , 0.33038541, 0.42267311,
+       0.48720353, 0.57339994, 0.76830045, 0.824279  , 0.92251664]), scaled_genome=None, log_likelihood=10487.648357644568, fitness_values=array([2.38559926, 1.29693248, 0.90765682, 0.66129135, 0.45673373,
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+       0.0579053 , 0.00296468, 0.02742847, 0.01187133, 0.01735672,
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+       0.36111947, 0.2257809 , 0.15729885, 0.10466003, 0.06978249]))]"
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+       0.25913193, 0.33561972, 0.43485383, 0.55195733, 0.57620665,
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+       0.69783039, 0.60730343, 0.55878862, 0.43360379, 0.32630552]))]"
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+       0.00481321, 0.01953271, 0.02162937, 0.00865861, 0.0176618 ,
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+       1.54783575, 1.54783575, 1.54783575, 1.54783575, 1.53377398]))]"
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+       0.25689987, 0.32965783, 0.43793375, 0.51460404, 0.5724963 ,
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+       0.02484872, 0.01884774, 0.01120346, 0.01670597, 0.03228822,
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+       0.89162736, 0.85556973, 0.62522036, 0.53213599, 0.4816414 ])), Solution(fitness=0.41389029218235013, genome=array([0.26038035, 0.07044721, 0.31773037, 0.24728925, 0.06881708,
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+       0.03255109, 0.03140554, 0.03931036, 0.03889396, 0.05853304,
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+       0.71999641, 0.86151262, 0.986176  , 1.10598904, 1.28810742]), scaled_genome=None, log_likelihood=4040.0741483302327, fitness_values=array([3.2101166 , 2.68550744, 1.48497598, 1.19609192, 1.0282298 ,
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+       0.03296873, 0.04853704, 0.02624761, 0.04190537, 0.05126946,
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+       0.87913725, 0.61997601, 0.53988172, 0.53949528, 0.43208885])), Solution(fitness=0.3497761062098629, genome=array([0.19018288, 0.14275919, 0.27506865, 0.27082795, 0.04545723,
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+       0.7038277 , 0.85443765, 0.96626394, 1.07067224, 1.27094404]), scaled_genome=None, log_likelihood=3892.747812207746, fitness_values=array([2.80075099, 2.3572698 , 1.33424566, 1.26311962, 1.00284176,
+       0.77704646, 0.61403388, 0.47889631, 0.41463893, 0.34977611])), Solution(fitness=0.4225795861162607, genome=array([0.25215039, 0.10412312, 0.28257309, 0.28642637, 0.09360919,
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+       0.75570472, 0.60528155, 0.50260181, 0.48569004, 0.39397457]))]"
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+       0.60047008, 0.7185904 , 0.82469919, 0.93716705, 1.03902701]), scaled_genome=None, log_likelihood=4472.447679324247, fitness_values=array([0.7937762 , 0.75925687, 0.62000993, 0.51096058, 0.43009208,
+       0.32508077, 0.25621214, 0.25272587, 0.19709992, 0.15366591])), Solution(fitness=0.19316063574547562, genome=array([0.14853793, 0.03579212, 0.2447468 , 0.14369034, 0.05926884,
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+       0.0458199 , 0.04147605, 0.02148067, 0.02509584, 0.03193453,
+       0.01366842, 0.1006541 , 0.24136499, 0.31812629, 0.45040767,
+       0.59859698, 0.71942543, 0.82908935, 0.94803884, 1.04593538]), scaled_genome=None, log_likelihood=4519.431449286922, fitness_values=array([1.39955383, 0.83956521, 0.72943517, 0.53743322, 0.50144915,
+       0.36308855, 0.31313536, 0.2533464 , 0.23212776, 0.19316064])), Solution(fitness=0.151040390032982, genome=array([0.15358723, 0.03252423, 0.20043947, 0.17276353, 0.05620153,
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+       0.05257005, 0.03037092, 0.0241629 , 0.04476658, 0.03383804,
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+       0.58929042, 0.72637281, 0.82743497, 0.97116926, 1.02957129]), scaled_genome=None, log_likelihood=4452.274024966357, fitness_values=array([0.99888309, 0.91057181, 0.55366445, 0.55366445, 0.33259573,
+       0.3078966 , 0.2614762 , 0.2052976 , 0.17618679, 0.15104039])), Solution(fitness=0.19618318271061305, genome=array([0.14281212, 0.04144011, 0.19023944, 0.14210537, 0.05309828,
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+       0.01509558, 0.0374952 , 0.01643704, 0.03443246, 0.03847706,
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+       0.1927481 , 0.18597394, 0.17160914, 0.17008831, 0.168779  ]))]"
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+       0.00517884, 0.19805351, 0.34446271, 0.40441283, 0.46933324,
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+       0.42770003, 0.48755899, 0.566356  , 0.65528467, 0.83620966]), scaled_genome=None, log_likelihood=6134.4249016467775, fitness_values=array([3.89736365, 3.68471983, 2.66390212, 2.66390212, 2.42473848,
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+       0.84130019, 0.80131072, 0.78342681, 0.72443483, 0.64803714]))]"
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+        9.16516339e-01,  9.67685856e-01]), scaled_genome=None, log_likelihood=6627.1784990907845, fitness_values=array([2.73057426, 2.73057426, 1.9398138 , 1.9398138 , 1.80201294,
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+       0.46489727, 0.39898092, 0.28384037, 0.18761619, 0.13561897]))]"
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+       0.3909306 , 0.23416767, 0.12954395, 0.06472801, 0.03087702])), Solution(fitness=0.05052905057869459, genome=array([ 0.11809286,  0.08438064,  0.12751614,  0.26776213,  0.03032962,
+        0.03183221,  0.04570727,  0.10421396,  0.12828188,  0.16256395,
+        0.01797054,  0.02191071,  0.06650615,  0.02084401,  0.04364187,
+        0.03114268,  0.02488363,  0.00380875,  0.00675292,  0.04111198,
+       -0.00196106,  0.19137401,  0.28514105,  0.38720366,  0.51466232,
+        0.62956138,  0.71351134,  0.86420566,  0.93670885,  0.98036499]), scaled_genome=None, log_likelihood=7307.357497493336, fitness_values=array([2.46346316, 1.88662158, 1.09314277, 0.68443086, 0.59547429,
+       0.39026175, 0.28001675, 0.14536247, 0.08275636, 0.05052905])), Solution(fitness=0.046495041619752236, genome=array([0.05284525, 0.11546792, 0.09558967, 0.26841438, 0.03391003,
+       0.03446653, 0.04246949, 0.12164447, 0.13955933, 0.13857212,
+       0.01210085, 0.02437339, 0.04651368, 0.02218977, 0.04720421,
+       0.02846364, 0.02305675, 0.00362101, 0.00724217, 0.04046702,
+       0.00989342, 0.18805808, 0.28355174, 0.38626337, 0.5369014 ,
+       0.64957156, 0.71612515, 0.86424469, 0.93691203, 0.98568473]), scaled_genome=None, log_likelihood=7288.727318734986, fitness_values=array([2.75438702, 1.58442179, 1.00508124, 0.81931861, 0.66931479,
+       0.47908648, 0.31296763, 0.19544449, 0.10553025, 0.04649504])), Solution(fitness=0.053925312686886204, genome=array([0.06917098, 0.12319366, 0.07228006, 0.29928187, 0.03132225,
+       0.03620433, 0.05000154, 0.11589885, 0.16164409, 0.15764467,
+       0.01488943, 0.02553156, 0.03884508, 0.02173568, 0.03504162,
+       0.03607142, 0.03017268, 0.00390068, 0.00731485, 0.04058475,
+       0.00621063, 0.19156853, 0.28229192, 0.38390869, 0.51405047,
+       0.61362095, 0.7090035 , 0.86411894, 0.93759814, 0.98221048]), scaled_genome=None, log_likelihood=7871.731908927502, fitness_values=array([2.3003605 , 1.60310836, 1.33850731, 0.83015759, 0.60396863,
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+       0.0300863 , 0.04936673, 0.1138505 , 0.17388989, 0.12083574,
+       0.00648537, 0.0227428 , 0.046078  , 0.02187084, 0.0359611 ,
+       0.02435398, 0.03821178, 0.00354514, 0.01243534, 0.03424994,
+       0.01344873, 0.18805369, 0.26945003, 0.38547058, 0.526391  ,
+       0.63179388, 0.71102275, 0.86410552, 0.93665082, 0.99284203]), scaled_genome=None, log_likelihood=7206.581344521121, fitness_values=array([3.39277044, 1.71633931, 1.30822483, 0.90050632, 0.70793517,
+       0.45415029, 0.27479664, 0.1915204 , 0.11493615, 0.06275249])), Solution(fitness=0.03275339846249643, genome=array([0.04719909, 0.10501197, 0.09650144, 0.26945134, 0.03230965,
+       0.03452849, 0.04443307, 0.1090075 , 0.11721272, 0.17690266,
+       0.00793174, 0.02214023, 0.05511549, 0.02132696, 0.04153152,
+       0.03484484, 0.02423483, 0.00354285, 0.00603886, 0.04234829,
+       0.01229839, 0.18875806, 0.29356906, 0.38683348, 0.53450099,
+       0.63708273, 0.71670562, 0.86420566, 0.93657522, 0.98248977]), scaled_genome=None, log_likelihood=7373.3966548171775, fitness_values=array([3.66486172, 1.88121991, 1.25031819, 0.75076924, 0.602044  ,
+       0.25941096, 0.21411128, 0.13770832, 0.0694897 , 0.0327534 ]))]"
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+       0.02881036, 0.01554855, 0.03732877, 0.01694223, 0.0455733 ,
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+       2.05850823, 1.80372635, 1.80372635, 1.2034547 , 1.2034547 ])), Solution(fitness=1.6213132122617975, genome=array([0.38591104, 0.12789536, 0.10912107, 0.24100824, 0.14399062,
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+       1.47733272, 1.24315637, 1.24315637, 1.24219986, 1.16389041]))]"
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+       0.03179066, 0.06422127, 0.13064869, 0.28422764, 0.08603227,
+       0.02484847, 0.02612663, 0.03573211, 0.0252705 , 0.03131698,
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+       0.0484972 , 0.02018499, 0.04796285, 0.0186792 , 0.026416  ,
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+       0.93486336, 0.76899582, 0.35328204, 0.22506909, 0.10686483]))]"
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+       0.34359869, 0.23667096, 0.13199499, 0.05770216, 0.04244511])), Solution(fitness=0.03563044929581789, genome=array([0.24500202, 0.09723765, 0.05919446, 0.08342331, 0.20246473,
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+       0.14988162, 0.21645397, 0.33737612, 0.45050148, 0.51055247,
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+        0.50467475,  0.57259628,  0.69963054,  0.75772673,  0.8738318 ]), scaled_genome=None, log_likelihood=6835.146032310279, fitness_values=array([1.99762507, 1.60302372, 1.40525534, 1.01764798, 0.59171973,
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+       0.0384684 , 0.04975954, 0.011471  , 0.02797447, 0.02319046,
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+       0.51330025, 0.46036036, 0.35797433, 0.26223184, 0.19885411]))]"
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+        0.04298453,  0.05042538,  0.0315697 ,  0.00423988,  0.02760861,
+        0.06473968,  0.00595761,  0.03243319,  0.02264043,  0.04948369,
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+       1.2039181 , 1.2039181 , 1.2039181 , 1.2039181 , 1.19075624]))]"
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+       0.43534471, 0.4026469 , 0.39668296, 0.39597205, 0.35481764]))]"
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+       1.2778412 , 1.2778412 , 1.0588128 , 1.0588128 , 1.0588128 ]))]"
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+       0.02725731, 0.04003391, 0.14724636, 0.26282092, 0.41876223,
+       0.49794509, 0.68468105, 0.82071184, 0.96838773]), scaled_genome=None, log_likelihood=6509.935032158302, fitness_values=array([4.74024933, 2.4027451 , 2.16925766, 2.16925766, 1.68311033,
+       1.68311033, 1.61059621, 1.28701742, 1.28701742, 1.28701742])), Solution(fitness=1.203996018895482, genome=array([0.27136746, 0.17081448, 0.05427853, 0.13870119, 0.05808945,
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+        0.18053241,  0.30023159,  0.39890427,  0.02934749,  0.03153783,
+        0.02671019,  0.04739605,  0.01513082,  0.04171099,  0.05056162,
+        0.0045406 , -0.01780627,  0.21141041,  0.26893466,  0.47540349,
+        0.55228606,  0.72186687,  0.87335946,  0.98132701]), scaled_genome=None, log_likelihood=8481.754524230575, fitness_values=array([2.29488874, 2.29488874, 2.29488874, 2.0905481 , 1.35168238,
+       1.06793543, 1.00306295, 0.96262482, 0.96026218, 0.96026218]))]"
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+       0.67062157, 0.72963564, 0.85054718, 0.96826724]), scaled_genome=None, log_likelihood=3833.846814304183, fitness_values=array([1.37885717, 1.06241918, 0.70194138, 0.5898832 , 0.41979213,
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+       0.50189471, 0.45832725, 0.37260978, 0.33345632, 0.31441228]))]"
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+       0.00522138, 0.03042291, 0.00873456, 0.01347596, 0.03981123,
+       0.06467735, 0.13900742, 0.26410663, 0.44504124, 0.57692732,
+       0.67525769, 0.68543771, 0.92008861, 1.02667205]), scaled_genome=None, log_likelihood=5384.1349096103195, fitness_values=array([0.96182772, 0.8206589 , 0.62004324, 0.39992522, 0.35877314,
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+       0.05874118, 0.14099896, 0.26238139, 0.44424786, 0.57618117,
+       0.67884517, 0.88728174, 0.93473225, 1.02191021]), scaled_genome=None, log_likelihood=5326.28876867918, fitness_values=array([1.15328384, 0.80506043, 0.64144137, 0.48307162, 0.3870953 ,
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+       0.27727222, 0.2357838 , 0.1956368 , 0.17906948, 0.16607579]))]"
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+       0.4305352 , 0.38859607, 0.32253822, 0.29865617, 0.28194982])), Solution(fitness=0.2909795963718617, genome=array([0.35105085, 0.03316359, 0.04912777, 0.31335359, 0.15332208,
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+        0.4058861 ,  0.47392883,  0.53766992,  0.62747864]), scaled_genome=None, log_likelihood=6221.48823951351, fitness_values=array([1.02227824, 0.61317906, 0.45391986, 0.41166508, 0.37371849,
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+       1.08279296, 0.93477206, 0.93477206, 0.93477206, 0.93477206])), Solution(fitness=0.6599166023091044, genome=array([ 0.40459058,  0.06912311,  0.03      ,  0.10867059,  0.35645122,
+        0.29936254,  0.26939983,  0.07282883,  0.06288508,  0.0195805 ,
+        0.0421623 ,  0.01911867,  0.02189961,  0.04875003,  0.02852708,
+        0.01751926, -0.0299198 ,  0.1649254 ,  0.35105938,  0.45072781,
+        0.56164787,  0.73079065,  0.88589623,  0.99238863]), scaled_genome=None, log_likelihood=5161.372953568985, fitness_values=array([3.43732218, 1.88485182, 1.21938623, 1.21938623, 1.21938623,
+       0.79356838, 0.79356838, 0.79356838, 0.6599166 , 0.6599166 ])), Solution(fitness=0.8322479047238031, genome=array([ 0.1755439 ,  0.19908078,  0.0498772 ,  0.20651301,  0.28228729,
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+        0.02437171,  0.05592629,  0.02135603,  0.05653175,  0.01123229,
+        0.0437052 , -0.0299198 ,  0.171282  ,  0.33434868,  0.47241571,
+        0.5476693 ,  0.78320724,  0.88642017,  0.9645413 ]), scaled_genome=None, log_likelihood=4865.085000406392, fitness_values=array([3.72049619, 2.61119805, 1.69753825, 1.13499257, 1.13499257,
+       1.13499257, 1.08520522, 1.08520522, 1.08520522, 0.8322479 ])), Solution(fitness=0.8662895182850558, genome=array([ 0.30764907,  0.16939653,  0.29923285,  0.3032219 ,  0.14125325,
+        0.18675755,  0.16290392,  0.16246596,  0.01019081,  0.02832346,
+        0.05883564,  0.01348349,  0.04354257,  0.01051541,  0.05366646,
+        0.06865528, -0.01300148,  0.1509995 ,  0.44453003,  0.56010209,
+        0.66395312,  0.71412037,  0.84583909,  0.98019714]), scaled_genome=None, log_likelihood=6771.911844241849, fitness_values=array([3.46374046, 1.98786446, 1.82731404, 1.67823586, 1.17518107,
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+       0.0169148 , 0.05808722, 0.0152175 , 0.04703064, 0.05779727,
+       0.05208739, 0.02412822, 0.21614242, 0.36817623, 0.48856058,
+       0.5591088 , 0.71906557, 0.84635489, 1.013602  ]), scaled_genome=None, log_likelihood=4502.104841307398, fitness_values=array([3.42444767, 2.65463907, 2.19892113, 2.19603452, 1.31285242,
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+       0.05976214, 0.01061896, 0.01383352, 0.06378798, 0.01996094,
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+       1.26419592, 1.26419592, 1.06639997, 1.06639997, 0.97391108])), Solution(fitness=0.7673619337326261, genome=array([ 0.40547777,  0.12469603,  0.04787114,  0.17681402,  0.24739658,
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+        0.5641547 ,  0.70709824,  0.85764754,  0.9941678 ]), scaled_genome=None, log_likelihood=3788.7455801484525, fitness_values=array([1.22456927, 1.22029154, 1.22029154, 1.22029154, 1.0497822 ,
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+        0.02067277, -0.02605577,  0.11892652,  0.29197536,  0.43641922,
+        0.56008041,  0.725185  ,  0.9198948 ,  1.00055888]), scaled_genome=None, log_likelihood=1103.2969435569157, fitness_values=array([3.1007905 , 2.95681761, 1.43411084, 1.43411084, 1.34500352,
+       1.03512546, 1.03512546, 1.03512546, 1.03512546, 1.02710541]))]"
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+       0.5384447 , 0.48964306, 0.43212717, 0.34151129, 0.34151129])), Solution(fitness=0.2552040960249152, genome=array([0.08872089, 0.11880899, 0.07045309, 0.12889249, 0.33213277,
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+       0.42747024, 0.40208894, 0.34340763, 0.287384  , 0.21349235])), Solution(fitness=0.10358260853048994, genome=array([6.39462856e-02, 9.94991203e-02, 7.82954653e-02, 9.70446029e-02,
+       3.24240612e-01, 1.47453572e-01, 9.96470827e-02, 8.98732591e-02,
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+       0.2847334 , 0.25424657, 0.18740685, 0.13840164, 0.13576317])), Solution(fitness=0.17221588695232243, genome=array([ 0.0491915 ,  0.08046771,  0.34142149,  0.33269661,  0.16118331,
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+        0.64124577,  0.86211775,  0.9794846 ,  1.17511906]), scaled_genome=None, log_likelihood=4091.77848142596, fitness_values=array([2.5047292 , 1.35589361, 1.00897391, 0.61190958, 0.56678962,
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+       0.03671525, 0.04499187, 0.17320611, 0.29607087, 0.39422379,
+       0.60459757, 0.77512904, 0.93014494, 1.01385088]), scaled_genome=None, log_likelihood=4473.947798241001, fitness_values=array([2.17629849, 1.40238008, 1.22300014, 0.91541176, 0.4973382 ,
+       0.4337654 , 0.4337654 , 0.40470766, 0.3426307 , 0.31496481])), Solution(fitness=0.26133835630251895, genome=array([0.03790632, 0.23625296, 0.25818261, 0.19443628, 0.08034491,
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+       0.03579086, 0.06483879, 0.16733629, 0.29683267, 0.39344324,
+       0.58508161, 0.72514049, 0.88001056, 1.01336328]), scaled_genome=None, log_likelihood=5140.45597430057, fitness_values=array([2.56405269, 1.78215561, 1.4047382 , 0.99168092, 0.76053881,
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+       0.09301637, 0.0782701 , 0.16656454, 0.04767727, 0.0366418 ,
+       0.00921181, 0.01256071, 0.05497081, 0.03538832, 0.04983836,
+       0.04490941, 0.08893625, 0.17569439, 0.29504953, 0.39063527,
+       0.60441182, 0.75283511, 0.86733866, 1.0079544 ]), scaled_genome=None, log_likelihood=5336.555379154747, fitness_values=array([2.78162262, 1.26149047, 0.98468578, 0.85914874, 0.56907961,
+       0.5629645 , 0.44659386, 0.39938412, 0.36037881, 0.27708056])), Solution(fitness=0.2611889374834878, genome=array([0.0305226 , 0.23650834, 0.22364063, 0.14211847, 0.07930849,
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+       0.01285857, 0.0097726 , 0.06426529, 0.0385481 , 0.02680447,
+       0.04265834, 0.03075345, 0.16371106, 0.29770492, 0.394208  ,
+       0.62833945, 0.7565218 , 0.86246927, 0.98237867]), scaled_genome=None, log_likelihood=4125.0969842966015, fitness_values=array([1.67795633, 1.08230405, 1.02149446, 0.80290087, 0.66562441,
+       0.40117924, 0.29561375, 0.27398878, 0.26525865, 0.26118894])), Solution(fitness=0.28902463182964166, genome=array([0.05714692, 0.22735283, 0.26599586, 0.14473436, 0.11568372,
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+       0.60463036, 0.76082427, 0.88949452, 1.00642573]), scaled_genome=None, log_likelihood=5019.593562039054, fitness_values=array([2.12827678, 2.12827678, 1.24685878, 0.96439   , 0.70758597,
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+       0.0399168 , 0.03517754, 0.16715369, 0.29542495, 0.38696261,
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+       0.48103941, 0.40607343, 0.40607343, 0.34316138, 0.28814165])), Solution(fitness=0.3246750611374211, genome=array([0.03      , 0.301883  , 0.33160272, 0.19269318, 0.08985151,
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+       0.03963757, 0.04392945, 0.16427692, 0.29357175, 0.39360492,
+       0.60253026, 0.76159944, 0.92063065, 1.00267868]), scaled_genome=None, log_likelihood=5734.063427653242, fitness_values=array([2.82917121, 2.18746829, 1.57934945, 1.19177554, 0.92811691,
+       0.76754805, 0.54530016, 0.46307394, 0.32467506, 0.32467506]))]"
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+       0.03934228, 0.04783427, 0.16630503, 0.29168478, 0.3912469 ,
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+       0.10526882, 0.03514637, 0.1615331 , 0.33510075, 0.58901338,
+       0.72682843, 0.87878542, 0.98349244, 1.0575881 ]), scaled_genome=None, log_likelihood=1261.2666260113383, fitness_values=array([1.60001789, 1.3256766 , 1.1703105 , 1.12603326, 1.10565762,
+       1.02809765, 0.96660722, 0.90058905, 0.88240048, 0.86823276])), Solution(fitness=0.26280446508393884, genome=array([0.04555465, 0.21836696, 0.21923525, 0.15779988, 0.06373743,
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+       0.08916204, 0.05276438, 0.18479453, 0.04379549, 0.03696274,
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+       0.05929159, 0.09847359, 0.17668836, 0.02056729, 0.03774455,
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+       0.5921702 , 0.7219201 , 0.84604712, 0.99303778]), scaled_genome=None, log_likelihood=4481.9710864445115, fitness_values=array([3.09725079, 1.2292873 , 1.2292873 , 1.09313071, 0.75882848,
+       0.53603402, 0.35576247, 0.22965589, 0.16869238, 0.15759293])), Solution(fitness=0.15494273605042727, genome=array([0.03014032, 0.24871359, 0.23637777, 0.16644729, 0.09630549,
+       0.08456738, 0.04953247, 0.18669044, 0.06108269, 0.03779054,
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+       0.6844347 , 0.46701473, 0.31266488, 0.1735712 , 0.15494274]))]"
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+        0.03763876,  0.01394277,  0.01174439,  0.00746248,  0.00802862,
+        0.06304955, -0.01498486,  0.22933623,  0.37065927,  0.46506504,
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+       0.02989266, 0.06583455, 0.21236217, 0.33093019, 0.42878325,
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+       0.02694085, 0.01873   , 0.13962588, 0.23828687, 0.45258097,
+       0.53347578, 0.59538971, 0.88159205, 0.98361743]), scaled_genome=None, log_likelihood=4488.394076693, fitness_values=array([1.47991714, 1.2431171 , 0.4703553 , 0.4703553 , 0.4703553 ,
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+        0.0559088 ,  0.01588178,  0.04583148,  0.00732603,  0.07583271,
+        0.02868519, -0.04788383,  0.1652923 ,  0.40279244,  0.4587167 ,
+        0.58737649,  0.68612213,  0.8263949 ,  0.97655881]), scaled_genome=None, log_likelihood=4721.063130692008, fitness_values=array([1.56928379, 0.87044265, 0.87044265, 0.78548658, 0.78548658,
+       0.60200039, 0.60200039, 0.59319492, 0.59319492, 0.59319492]))]"
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+       0.97662496, 0.97662496, 0.97662496, 0.85121969, 0.85121969]))]"
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+       0.03681722, 0.07358261, 0.13890652, 0.28509115, 0.38510051,
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+       0.03459181, 0.07908517, 0.13939012, 0.27971382, 0.38620342,
+       0.56113762, 0.58966339, 0.77017481, 0.8932077 ]), scaled_genome=None, log_likelihood=3968.312932685432, fitness_values=array([1.47199305, 0.83879684, 0.77203464, 0.50670806, 0.50579868,
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+       0.07302099, 0.03210051, 0.00169793, 0.01058989, 0.03004497,
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+       0.35403839, 0.30676965, 0.19880237, 0.06867086, 0.02517836]))]"
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+       1.2378479 , 1.14954887, 1.13605017, 0.80310176, 0.80310176])), Solution(fitness=0.9440993962473939, genome=array([0.08312397, 0.03      , 0.08304596, 0.1577715 , 0.43929844,
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+       4.25287847e-01, 2.27766295e-01, 5.66056552e-02, 3.00326186e-01,
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+       2.58845016e-01, 3.45719794e-01, 4.72764946e-01, 4.77760903e-01,
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+       0.01798815, 0.2412398 , 0.32626419, 0.45538713, 0.55748323,
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+       0.01425243, 0.21449278, 0.34066694, 0.46710928, 0.55754492,
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+       0.01252452, 0.21907458, 0.32390131, 0.4956427 , 0.5421043 ,
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+       0.54155887, 0.48866201, 0.42889492, 0.39702208, 0.36310184]))]"
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+       0.54797518, 0.51645684, 0.35600633, 0.28888117, 0.27357892])), Solution(fitness=0.27120512115184914, genome=array([0.03028214, 0.03004542, 0.03016139, 0.05317513, 0.52671489,
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+       0.03957383, 0.28874211, 0.47947339, 0.57664875, 0.66126146,
+       0.79295462, 0.89780233, 0.94570747, 1.03172414]), scaled_genome=None, log_likelihood=4852.064212818986, fitness_values=array([1.61411633, 0.69287865, 0.63642679, 0.428524  , 0.26181076,
+       0.17536794, 0.13735791, 0.1112397 , 0.09809448, 0.09539889])), Solution(fitness=0.09641318748450417, genome=array([0.18391117, 0.03713712, 0.12072225, 0.14362745, 0.28979163,
+       0.1622518 , 0.08339878, 0.03521124, 0.04477243, 0.03659262,
+       0.0368322 , 0.04081961, 0.0296593 , 0.0036723 , 0.04349009,
+       0.03235076, 0.28790512, 0.48740553, 0.56929096, 0.65204092,
+       0.7935322 , 0.89771285, 0.95817862, 1.04764393]), scaled_genome=None, log_likelihood=4796.160886691964, fitness_values=array([1.72723172, 0.66361343, 0.63177736, 0.39509031, 0.26534008,
+       0.20122888, 0.1409153 , 0.11449499, 0.10052071, 0.09641319])), Solution(fitness=0.09567989259060367, genome=array([0.18641755, 0.04770732, 0.1160522 , 0.13564389, 0.29336516,
+       0.15476278, 0.08542739, 0.03609087, 0.04451999, 0.04067094,
+       0.03533406, 0.03733182, 0.03072251, 0.00361579, 0.04594058,
+       0.03373975, 0.28868538, 0.49868282, 0.57446768, 0.65370887,
+       0.79226246, 0.89782091, 0.95439079, 1.04526337]), scaled_genome=None, log_likelihood=4796.106389550785, fitness_values=array([1.56887732, 0.89146973, 0.45243897, 0.31879627, 0.22845107,
+       0.1690889 , 0.1340119 , 0.10617191, 0.09758661, 0.09567989])), Solution(fitness=0.09545771335594341, genome=array([0.18801958, 0.03720818, 0.1329577 , 0.12736348, 0.28632942,
+       0.1565164 , 0.0762021 , 0.04483438, 0.04489269, 0.03461435,
+       0.03768312, 0.03743042, 0.03048248, 0.00359059, 0.04140016,
+       0.0370531 , 0.28846458, 0.48825553, 0.57390976, 0.65602268,
+       0.79248448, 0.89780278, 0.94926355, 1.03847981]), scaled_genome=None, log_likelihood=4757.693796803431, fitness_values=array([1.4724295 , 0.7752239 , 0.44589432, 0.4340585 , 0.25938915,
+       0.19125874, 0.14055237, 0.10596877, 0.09755385, 0.09545771])), Solution(fitness=0.10255613997390119, genome=array([0.1869827 , 0.03486156, 0.10244784, 0.16519211, 0.28581501,
+       0.15776918, 0.11538401, 0.03007004, 0.04446913, 0.03372529,
+       0.03442041, 0.04267338, 0.02969526, 0.00364469, 0.05818374,
+       0.09838107, 0.28822728, 0.48309251, 0.56177861, 0.64538884,
+       0.79262509, 0.89788522, 0.97338969, 1.19319803]), scaled_genome=None, log_likelihood=4844.076532650758, fitness_values=array([2.11559482, 0.93952673, 0.57089544, 0.30357079, 0.23764936,
+       0.17700303, 0.13842985, 0.11508505, 0.10498904, 0.10255614])), Solution(fitness=0.09552528995020065, genome=array([0.18950029, 0.03437208, 0.15480825, 0.10947785, 0.29471364,
+       0.15987931, 0.06461722, 0.05430238, 0.04530212, 0.0330858 ,
+       0.03977784, 0.0332303 , 0.03067189, 0.00364576, 0.03607779,
+       0.03927583, 0.28868251, 0.48473313, 0.57744847, 0.66151362,
+       0.79261302, 0.89780273, 0.94466357, 1.03125076]), scaled_genome=None, log_likelihood=4847.4977254049445, fitness_values=array([1.18388091, 0.70634799, 0.47779749, 0.31909958, 0.23306591,
+       0.14259039, 0.11554761, 0.10123521, 0.0968771 , 0.09552529])), Solution(fitness=0.0807511641843494, genome=array([0.10331591, 0.08672562, 0.10289046, 0.20511358, 0.28930629,
+       0.15509315, 0.06701456, 0.0532458 , 0.04577914, 0.02161706,
+       0.0560605 , 0.05223722, 0.02968728, 0.00362156, 0.03806044,
+       0.03931618, 0.24708123, 0.31640327, 0.53281505, 0.6323132 ,
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+       0.19120836, 0.14931932, 0.10601845, 0.0831341 , 0.08075116])), Solution(fitness=0.09582765959408829, genome=array([0.18618232, 0.03453551, 0.12261298, 0.14153848, 0.28687788,
+       0.15349487, 0.09082976, 0.03536489, 0.0439523 , 0.03482101,
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+       0.03343691, 0.28764429, 0.48345717, 0.56952392, 0.65266137,
+       0.79340997, 0.89788432, 0.95343832, 1.04597571]), scaled_genome=None, log_likelihood=4769.981842504446, fitness_values=array([1.57344792, 0.99143884, 0.47440735, 0.45303077, 0.28520498,
+       0.20947672, 0.14525102, 0.11231502, 0.09968055, 0.09582766])), Solution(fitness=0.09500454417974823, genome=array([0.18432331, 0.03262178, 0.16412965, 0.10257203, 0.29136595,
+       0.15594418, 0.07738647, 0.04393609, 0.04421874, 0.03436984,
+       0.04274564, 0.03390501, 0.02989435, 0.00361985, 0.04252472,
+       0.03767263, 0.28920662, 0.48337533, 0.58008091, 0.66404502,
+       0.7930711 , 0.8978547 , 0.94898042, 1.03774912]), scaled_genome=None, log_likelihood=4788.564995678689, fitness_values=array([1.58276567, 0.90239599, 0.46091857, 0.36345334, 0.246831  ,
+       0.16131841, 0.12851232, 0.10175223, 0.09660938, 0.09500454]))]"
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+       0.05855814, 0.25778478, 0.3287307 , 0.49834318, 0.6611568 ,
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+       0.83645416, 0.79048994, 0.72396373, 0.72396373, 0.72396373])), Solution(fitness=0.4933405448690377, genome=array([0.21510285, 0.03584785, 0.21485828, 0.20001103, 0.10701398,
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+       0.03728939, 0.26309178, 0.41833986, 0.53710204, 0.63044554,
+       0.7973084 , 0.89217374, 0.96275701, 1.10917722]), scaled_genome=None, log_likelihood=5002.783675819686, fitness_values=array([0.92551252, 0.92551252, 0.85348777, 0.61550869, 0.61550869,
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+2,synthetic_8_40,SHADESolver,0.16079591821383793,0.020285635103027146,"[0.15948809 0.13949689 0.17246246 0.13796206 0.14621035 0.160873
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+       0.79016221, 0.89653972, 0.96779696, 1.06972811]), scaled_genome=None, log_likelihood=5135.619589959558, fitness_values=array([1.794587  , 1.36193309, 0.79226925, 0.58192867, 0.4292364 ,
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+       0.25516182, 0.2067164 , 0.1646768 , 0.15408035, 0.13796206])), Solution(fitness=0.14621034706374897, genome=array([0.17311124, 0.03      , 0.13555673, 0.16254929, 0.26880591,
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+       0.27709761, 0.22742004, 0.20102937, 0.17036708, 0.160873  ])), Solution(fitness=0.21259953358879755, genome=array([0.21015825, 0.035312  , 0.11108488, 0.15866153, 0.26619153,
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+       0.04317232, 0.03376378, 0.03621878, 0.00486801, 0.04171858,
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+       0.02472275, 0.28574891, 0.42632357, 0.5654839 , 0.65538799,
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+       0.05718734, 0.0603981 , 0.16279113, 0.21825328, 0.42423343,
+       0.5679019 , 0.74173609, 0.82011571, 0.94162322]), scaled_genome=None, log_likelihood=3654.5527884136013, fitness_values=array([1.96154842, 1.18699123, 0.84204537, 0.6294482 , 0.46740558,
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+       0.00143769, 0.02616337, 0.17953349, 0.35223131, 0.52342265,
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+       0.2380108 , 0.16937187, 0.20567451, 0.011669  , 0.07290334,
+       0.03080257, 0.03868034, 0.05810266, 0.02285384, 0.0258676 ,
+       0.07684943, 0.00490172, 0.09913785, 0.30290174, 0.41421664,
+       0.62844362, 0.83888415, 0.78238989, 1.0352129 ]), scaled_genome=None, log_likelihood=1829.8569889825285, fitness_values=array([1.27948594, 1.04258629, 0.76863544, 0.76863544, 0.58377785,
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+       0.01818034, 0.03554917, 0.04790565, 0.07012493, 0.02770018,
+       0.0095796 , 0.00918619, 0.12603137, 0.26856958, 0.40573059,
+       0.58276365, 0.8228581 , 1.03116854, 1.12920779]), scaled_genome=None, log_likelihood=597.0451127080909, fitness_values=array([1.31567697, 0.72783277, 0.72783277, 0.72783277, 0.72783277,
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+       0.0571705 , 0.08508842, 0.23654581, 0.30859741, 0.41875869,
+       0.60595883, 0.78319635, 0.85220931, 0.99305133]), scaled_genome=None, log_likelihood=1127.2474534341345, fitness_values=array([1.42100294, 1.15507806, 0.96716837, 0.57659892, 0.57659892,
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+       0.67242095, 0.83698413, 0.93699003, 1.07274974]), scaled_genome=None, log_likelihood=857.0829045327006, fitness_values=array([1.42800166, 1.42800166, 1.03872871, 0.84795896, 0.78665312,
+       0.78665312, 0.69165051, 0.68296116, 0.50703029, 0.50703029]))]"
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+       0.62823466, 0.82109952, 0.91713738, 1.10635058]), scaled_genome=None, log_likelihood=1647.773732006667, fitness_values=array([0.907669  , 0.72184989, 0.47046006, 0.44933046, 0.39564241,
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+       0.03661389, 0.02362012, 0.14903753, 0.29290475, 0.4130475 ,
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+       0.30223592, 0.25440535, 0.24426173, 0.21040903, 0.19821308])), Solution(fitness=0.17830727658356174, genome=array([0.18052136, 0.10270089, 0.0945592 , 0.0734089 , 0.21236772,
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+       0.31416849, 0.25027258, 0.20404032, 0.19293815, 0.157532  ])), Solution(fitness=0.211021127537141, genome=array([0.09349733, 0.14798256, 0.12648674, 0.09061979, 0.14739809,
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+       0.2945498 , 0.28041595, 0.26057388, 0.25324146, 0.23136418])), Solution(fitness=0.227984346613532, genome=array([ 0.04832469,  0.10563002,  0.11445552,  0.08812767,  0.07781967,
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+        0.10474038, -0.11764293,  0.02384751,  0.12568592,  0.27418489,
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+       0.21854871, 0.20102975, 0.1944892 , 0.18489779, 0.17204731])), Solution(fitness=0.16249518732888413, genome=array([0.07668212, 0.1277978 , 0.09736454, 0.07923203, 0.16380076,
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+       0.28032139, 0.23662415, 0.20351925, 0.17883911, 0.176051  ])), Solution(fitness=0.17463473548346145, genome=array([0.07944312, 0.0383602 , 0.03      , 0.03      , 0.06415936,
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+       0.04222389, 0.02197059, 0.02872102, 0.00772349, 0.03836609,
+       0.04224016, 0.080942  , 0.17642149, 0.32255822, 0.41266821,
+       0.54748372, 0.59321622, 0.68538218, 0.85850388]), scaled_genome=None, log_likelihood=8283.03730013702, fitness_values=array([1.99791207, 1.4924114 , 0.78337042, 0.5204245 , 0.42593686,
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+       0.54476422, 0.59198709, 0.68121675, 0.85653411]), scaled_genome=None, log_likelihood=8332.10305731171, fitness_values=array([2.27964596, 0.87729969, 0.81824689, 0.48237157, 0.3922501 ,
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+       0.311382  , 0.23431331, 0.19710963, 0.19426445, 0.16918097]))]"
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+       0.04960562, 0.050815  , 0.10253038, 0.186177  , 0.3755527 ,
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+       0.04566231, 0.00574439, 0.08737527, 0.27236727, 0.34095543,
+       0.45721556, 0.59963997, 0.79563805, 0.97587903]), scaled_genome=None, log_likelihood=3862.5602902620653, fitness_values=array([2.60814864, 1.56145968, 1.07762268, 1.07762268, 0.78686286,
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+       0.02046748, 0.00206171, 0.10213696, 0.25433839, 0.39678356,
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+       0.06176609, 0.03884527, 0.08517335, 0.01297467, 0.05116803,
+       0.03412017, 0.04409369, 0.09867043, 0.17119018, 0.34798899,
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+       0.03945723, 0.03898821, 0.10422765, 0.26945098, 0.35886226,
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+        0.01368521,  0.04328473,  0.03554351,  0.04131949,  0.01114557,
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+        0.51834912,  0.67933976,  0.73554925,  0.94878644]), scaled_genome=None, log_likelihood=3638.346402929532, fitness_values=array([1.88644057, 1.68319119, 1.18902362, 0.94476822, 0.94476822,
+       0.92074118, 0.89886344, 0.81681417, 0.81681417, 0.81681417]))]"
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+       0.0221981 , 0.02738344, 0.09898138, 0.3218829 , 0.41767191,
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+       0.44473703, 0.31906504, 0.19612201, 0.08853378, 0.043888  ]))]"
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+       0.50764253, 0.64745876, 0.7792683 , 0.90706056]), scaled_genome=None, log_likelihood=5388.871813528125, fitness_values=array([3.45567852, 2.01393336, 1.58631213, 1.28256293, 1.24400347,
+       1.16485391, 0.98632178, 0.98632178, 0.94755968, 0.90298801])), Solution(fitness=0.905907418338974, genome=array([0.14099835, 0.03886541, 0.08613388, 0.12069947, 0.12005317,
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+       0.02785902, 0.02844133, 0.03536699, 0.02834367, 0.02210281,
+       0.03707859, 0.02545992, 0.19277649, 0.32777878, 0.38238784,
+       0.5209108 , 0.6603399 , 0.82443502, 0.91004723]), scaled_genome=None, log_likelihood=5179.136972009105, fitness_values=array([3.33845882, 1.91902322, 1.77328873, 1.42634539, 1.24319749,
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+       0.02944648, 0.02318077, 0.02545568, 0.02795947, 0.00380817,
+       0.05848727, 0.02967037, 0.14627321, 0.32277617, 0.40073987,
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+       0.03016869, 0.02987997, 0.02401532, 0.06118371, 0.0080524 ,
+       0.0475907 , 0.01201846, 0.190933  , 0.3465234 , 0.44526767,
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+       0.04741967, 0.01918073, 0.120111  , 0.34515253, 0.47979146,
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+       0.03822023, 0.01166577, 0.11185354, 0.31109978, 0.37016694,
+       0.51477303, 0.64787841, 0.86671679, 0.92572596]), scaled_genome=None, log_likelihood=4677.347961992239, fitness_values=array([3.07772777, 2.36525498, 2.04322519, 1.60105562, 1.33659855,
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+       0.25933162, 0.22807096, 0.19969764, 0.18057036, 0.115491  ])), Solution(fitness=0.10113314050863577, genome=array([0.13792703, 0.08008334, 0.13410291, 0.08021188, 0.12888418,
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+       0.29511924, 0.24241956, 0.19731236, 0.14966944, 0.13593836]))]"
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+       0.02247388, 0.03004311, 0.15186807, 0.26464429, 0.34924072,
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+       0.0215857 , 0.02271084, 0.13527087, 0.26499583, 0.33740556,
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+       0.22028189, 0.1527948 , 0.07728387, 0.05467543, 0.04412398]))]"
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+       0.47545376, 0.57817427, 0.80422409, 0.9690947 ]), scaled_genome=None, log_likelihood=8058.2982612869855, fitness_values=array([8.27252664, 2.85869829, 2.34293335, 1.39685417, 0.71479173,
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+       0.50625275, 0.29984438, 0.1879814 , 0.09980306, 0.05126451]))]"
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+       0.2621547 , 0.19714076, 0.37470025, 0.05904179, 0.00858592,
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+       0.0060708 , 0.0237272 , 0.16138824, 0.22322641, 0.40453169,
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+       1.87393841, 1.44277652, 0.54983586, 0.523669  , 0.47963606])), Solution(fitness=0.4544408266859239, genome=array([0.09479943, 0.26407368, 0.15944292, 0.05199858, 0.1237451 ,
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+       1.10216916, 0.87632156, 0.63124349, 0.46576332, 0.45444083]))]"
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+       2.087449  , 1.89459813, 1.86816959, 1.5266008 , 1.5266008 ])), Solution(fitness=0.9712319073751026, genome=array([0.40844386, 0.25534649, 0.26457566, 0.31883594, 0.07226552,
+       0.19247646, 0.11256939, 0.08047424, 0.01754346, 0.01188347,
+       0.05283487, 0.00967014, 0.06924106, 0.0124202 , 0.02469158,
+       0.02077919, 0.00850969, 0.06514407, 0.18582027, 0.34502295,
+       0.47428255, 0.71878243, 0.87084671, 0.92094604]), scaled_genome=None, log_likelihood=11432.097194876362, fitness_values=array([3.5288867 , 3.5288867 , 3.5288867 , 2.16013532, 1.91775889,
+       1.82058781, 1.49281887, 1.49281887, 1.49281887, 0.97123191])), Solution(fitness=1.1829827785485199, genome=array([0.33119091, 0.32114232, 0.13579674, 0.58493693, 0.09926731,
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+       0.0436394 , 0.01028348, 0.03101942, 0.01062549, 0.06822101,
+       0.05876018, 0.03186356, 0.07266506, 0.2127044 , 0.34519523,
+       0.46697326, 0.58940757, 0.76699987, 0.91021512]), scaled_genome=None, log_likelihood=10816.349420226175, fitness_values=array([3.94445308, 3.40402009, 2.5304857 , 2.5304857 , 1.77467519,
+       1.19955803, 1.19955803, 1.19955803, 1.18298278, 1.18298278])), Solution(fitness=1.2702201258987424, genome=array([0.47148587, 0.20654643, 0.10925259, 0.24311089, 0.09677216,
+       0.03      , 0.10017806, 0.093692  , 0.02529167, 0.03143198,
+       0.06273756, 0.01214325, 0.02927588, 0.06927834, 0.01732875,
+       0.04112641, 0.03228956, 0.1215866 , 0.24196172, 0.34491853,
+       0.50811476, 0.60552383, 0.73385673, 0.86709854]), scaled_genome=None, log_likelihood=8850.331947757884, fitness_values=array([3.57577526, 2.18094259, 2.13665844, 2.13665844, 1.86443499,
+       1.55701454, 1.53142007, 1.27022013, 1.27022013, 1.27022013])), Solution(fitness=1.1842569623442665, genome=array([0.27123916, 0.15186871, 0.13850425, 0.21782587, 0.13024069,
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+       0.04167955, 0.00939621, 0.0274501 , 0.02111954, 0.05613177,
+       0.043227  , 0.06060176, 0.20155274, 0.3022511 , 0.34661811,
+       0.46746732, 0.54710615, 0.74122321, 0.86168943]), scaled_genome=None, log_likelihood=8927.97194459999, fitness_values=array([2.39529975, 2.22728173, 2.22728173, 2.22728173, 2.09559167,
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+       0.12865486, 0.08557036, 0.06458571, 0.00555094, 0.04250839,
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+       0.01635189, 0.06119143, 0.08099699, 0.28247585, 0.35041786,
+       0.46197857, 0.57935334, 0.76676439, 0.90092347]), scaled_genome=None, log_likelihood=10238.1490985099, fitness_values=array([3.40628099, 3.40628099, 2.05246109, 2.05246109, 2.05246109,
+       1.75819706, 1.16532616, 1.16532616, 1.16532616, 1.16532616]))]"
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+       0.49348044, 0.62955169, 0.72708387, 0.88784796]), scaled_genome=None, log_likelihood=11362.652603883245, fitness_values=array([2.5260762 , 2.5260762 , 1.58780367, 0.99980933, 0.92706865,
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+       0.04772573, 0.04998505, 0.11020598, 0.26277971, 0.343594  ,
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+       0.76765829, 0.6702885 , 0.58987221, 0.55106626, 0.51943878])), Solution(fitness=0.5140549715947837, genome=array([0.31793622, 0.05441634, 0.15958646, 0.37832826, 0.07402255,
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+       0.04084313, 0.00795059, 0.06534541, 0.0202459 , 0.01840423,
+       0.02950492, 0.05543849, 0.14682395, 0.24291855, 0.34294844,
+       0.49159482, 0.56956632, 0.72683387, 0.88160837]), scaled_genome=None, log_likelihood=10858.791278950623, fitness_values=array([1.75638614, 1.75638614, 1.37816224, 1.11220044, 0.94817663,
+       0.7981064 , 0.63581893, 0.62483476, 0.56109795, 0.51405497])), Solution(fitness=0.4562986675564583, genome=array([0.32101384, 0.09684234, 0.05624209, 0.46666924, 0.0933043 ,
+       0.03766113, 0.10371592, 0.08450926, 0.02830719, 0.01021772,
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+       0.47757862, 0.62356835, 0.73245448, 0.86848745]), scaled_genome=None, log_likelihood=11282.304997374997, fitness_values=array([3.46461972, 2.35440368, 1.59896145, 1.23013301, 1.17187255,
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+       0.03680086, 0.05919404, 0.10045522, 0.02845827, 0.03316661,
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+       0.6793923 , 0.54706777, 0.54706777, 0.54706777, 0.50247692])), Solution(fitness=0.4011754834327152, genome=array([0.37836299, 0.07550775, 0.11041454, 0.41264481, 0.05281529,
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+       0.45962982, 0.59135164, 0.72192579, 0.8831677 ]), scaled_genome=None, log_likelihood=11634.62297305671, fitness_values=array([3.08015321, 1.76685112, 1.39063634, 1.01021969, 0.91678188,
+       0.75084917, 0.64762806, 0.55740331, 0.46357377, 0.43152139]))]"
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+        0.03684503,  0.03353964,  0.00804979,  0.08312775,  0.01854723,
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+        0.03220237,  0.00908555,  0.07468782,  0.03705112,  0.01268787,
+        0.0354264 , -0.01730488,  0.06238615,  0.2464287 ,  0.3419221 ,
+        0.43467009,  0.55676197,  0.71689043,  0.88594671]), scaled_genome=None, log_likelihood=9504.188579501637, fitness_values=array([2.03899503, 1.14886579, 0.8496038 , 0.73709097, 0.64181323,
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+       0.0279264 , 0.02143137, 0.06314915, 0.2332294 , 0.34478209,
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+       0.47336176, 0.41534192, 0.41108565, 0.39986012, 0.3820948 ])), Solution(fitness=0.412214319561755, genome=array([ 0.04070446,  0.28695843,  0.12787507,  0.3476612 ,  0.04778987,
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+       0.5594246 , 0.5594246 , 0.55226048, 0.54963594, 0.54655709])), Solution(fitness=0.37920351871926655, genome=array([0.32337439, 0.0639741 , 0.04912899, 0.36353267, 0.03533002,
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+       0.5877404 , 0.47205361, 0.45663873, 0.39975751, 0.37920352]))]"
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+        0.08873138,  0.00586088,  0.005615  ,  0.09898987,  0.0308491 ,
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+       0.55827836, 0.39838552, 0.27736669, 0.1741873 , 0.11481635]))]"
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+       0.01015447, 0.02542817, 0.02126922, 0.02771089, 0.05374231,
+       0.01979947, 0.05915162, 0.18200491, 0.30869616, 0.37906088,
+       0.49390693, 0.56854385, 0.73081249, 0.92531997]), scaled_genome=None, log_likelihood=4963.50180170225, fitness_values=array([1.14780333, 0.69482855, 0.6418657 , 0.52754562, 0.38728675,
+       0.34391633, 0.30752197, 0.25678948, 0.21651153, 0.18506151])), Solution(fitness=0.1700230875860479, genome=array([0.06414115, 0.14188503, 0.16697633, 0.04538261, 0.25543306,
+       0.24311916, 0.06725828, 0.12739847, 0.03298282, 0.03879422,
+       0.01099304, 0.05094628, 0.0166353 , 0.04066537, 0.04828233,
+       0.02696884, 0.05383442, 0.17074588, 0.308792  , 0.36272178,
+       0.48693511, 0.56867498, 0.73477283, 0.91122435]), scaled_genome=None, log_likelihood=5135.717786743024, fitness_values=array([1.30913741, 0.92662088, 0.60676829, 0.40823635, 0.38944207,
+       0.28204403, 0.27902656, 0.22796718, 0.1840779 , 0.17002309])), Solution(fitness=0.1777681011198637, genome=array([0.07510981, 0.14957269, 0.18924096, 0.0519782 , 0.21239953,
+       0.22887413, 0.0493164 , 0.11010113, 0.03087655, 0.04126769,
+       0.00904579, 0.04276042, 0.01824322, 0.0349703 , 0.03003313,
+       0.03360944, 0.06733362, 0.18183219, 0.31003782, 0.35826139,
+       0.49154304, 0.56058382, 0.73135601, 0.92101361]), scaled_genome=None, log_likelihood=4729.0242746530475, fitness_values=array([1.1861783 , 0.78772742, 0.65851411, 0.45253183, 0.42064118,
+       0.32916551, 0.27838909, 0.25078502, 0.22089737, 0.1777681 ])), Solution(fitness=0.17711386104604673, genome=array([0.08851092, 0.16754248, 0.17156164, 0.03852559, 0.24992132,
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+       0.00742002, 0.03882488, 0.02164303, 0.03842582, 0.04186488,
+       0.02882707, 0.05818784, 0.20025719, 0.30955633, 0.36947003,
+       0.49081952, 0.57109723, 0.73212342, 0.91969419]), scaled_genome=None, log_likelihood=4987.118248628902, fitness_values=array([0.94986226, 0.80583428, 0.5564586 , 0.47845964, 0.3624037 ,
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+       0.19222442, 0.07638352, 0.11359636, 0.02761231, 0.03938581,
+       0.00934366, 0.04965522, 0.01866137, 0.03333226, 0.03387537,
+       0.03017353, 0.05862424, 0.16752657, 0.30782285, 0.36225153,
+       0.49014892, 0.56636565, 0.72062041, 0.91438339]), scaled_genome=None, log_likelihood=5726.319606738551, fitness_values=array([1.20487875, 0.88371703, 0.60664261, 0.42060991, 0.3717004 ,
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+       0.01031227, 0.04903541, 0.01765423, 0.04561981, 0.04622805,
+       0.02004851, 0.06187957, 0.1813251 , 0.30780895, 0.36191374,
+       0.49119603, 0.5610626 , 0.74275121, 0.92084846]), scaled_genome=None, log_likelihood=5417.035708946159, fitness_values=array([1.87062487, 1.02306538, 0.71729607, 0.56216737, 0.36791056,
+       0.33918936, 0.26036468, 0.20170927, 0.18736456, 0.16904858])), Solution(fitness=0.16701736906277043, genome=array([0.06128697, 0.17596675, 0.17398461, 0.04631244, 0.23284353,
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+       0.01151776, 0.04628803, 0.01844482, 0.04212092, 0.04080828,
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+       0.01414935, 0.04076178, 0.02161088, 0.03413072, 0.04801627,
+       0.0220425 , 0.05966302, 0.19349181, 0.31112497, 0.37233094,
+       0.49620031, 0.56952863, 0.73653099, 0.91562266]), scaled_genome=None, log_likelihood=4777.012807560062, fitness_values=array([1.09487134, 1.07252605, 0.61172744, 0.53461726, 0.42439344,
+       0.38859408, 0.3441459 , 0.24522913, 0.23579069, 0.21671037]))]"
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+       0.01683692, 0.06188966, 0.17515577, 0.30250163, 0.36544779,
+       0.51755283, 0.6376919 , 0.8204227 , 0.9159119 ]), scaled_genome=None, log_likelihood=3580.2373391471956, fitness_values=array([0.69483621, 0.50243142, 0.44106139, 0.42265144, 0.38487213,
+       0.37340401, 0.3643254 , 0.35789713, 0.34893587, 0.34823552])), Solution(fitness=0.3426473143388892, genome=array([0.07536074, 0.09287292, 0.18247381, 0.07451396, 0.33282527,
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+       0.02053454, 0.07825449, 0.17759198, 0.30167863, 0.40419361,
+       0.51136913, 0.61721629, 0.74172372, 0.9144027 ]), scaled_genome=None, log_likelihood=3609.8244849714956, fitness_values=array([0.71820194, 0.58754699, 0.51829358, 0.45214097, 0.40307774,
+       0.38394637, 0.37819345, 0.36607192, 0.36049677, 0.34264731])), Solution(fitness=0.4134311723953243, genome=array([0.06970905, 0.09295917, 0.20888364, 0.08546983, 0.34693513,
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+       0.0219409 , 0.07240911, 0.17980229, 0.30394576, 0.36517534,
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+       0.02657385, 0.03031154, 0.03378312, 0.02897589, 0.05851907,
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+       0.0042415 , 0.06882201, 0.19784881, 0.45295495, 0.4407681 ,
+       0.56549204, 0.68167625, 0.86234744, 0.90372901]), scaled_genome=None, log_likelihood=14115.146886353254, fitness_values=array([2.73916695, 2.56737502, 2.14926951, 1.49619193, 0.85279663,
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+       0.00405367, 0.06345225, 0.19777231, 0.51654807, 0.44112717,
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+       0.00423015, 0.05957316, 0.19785527, 0.20328149, 0.44083395,
+       0.56541069, 0.68297121, 0.89329458, 0.90365748]), scaled_genome=None, log_likelihood=14009.77687122892, fitness_values=array([3.96061113, 3.38879349, 2.93033215, 1.33508085, 1.16221466,
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+       0.0563912 , 0.0059243 , 0.01345946, 0.02172586, 0.07186547,
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+       0.00433655, 0.06358049, 0.19787737, 0.4730151 , 0.4405704 ,
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+       0.56616285, 0.67483438, 0.88545946, 0.9037482 ]), scaled_genome=None, log_likelihood=13951.464871643226, fitness_values=array([2.34313058, 2.34313058, 2.33892148, 1.35690237, 0.75642707,
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+       0.00433447, 0.06307722, 0.19777909, 0.27307097, 0.44083164,
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+       0.51438977, 0.34502047, 0.16977382, 0.10512155, 0.07669328]))]"
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+       0.43504626, 0.3481079 , 0.32102728, 0.29720416, 0.28387738]))]"
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+       0.06009211, 0.00573519, 0.23006464, 0.42722426, 0.59460459,
+       0.69432973, 0.82261133, 0.98264479, 1.08102283]), scaled_genome=None, log_likelihood=2569.2624962820432, fitness_values=array([7.06164387, 3.85559861, 2.13051284, 2.13051284, 2.13051284,
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+       0.18436204, 0.03      , 0.03      , 0.01910393, 0.06499933,
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+       0.01956399, 0.01566347, 0.23548291, 0.41299277, 0.55972503,
+       0.66703207, 0.84560325, 0.97663906, 1.14640413]), scaled_genome=None, log_likelihood=4485.42733823939, fitness_values=array([4.43249266, 4.23215606, 1.8985847 , 1.8985847 , 1.55559347,
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+       0.0502659 , 0.00573519, 0.20164382, 0.44288244, 0.59335451,
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+       1.54434761, 1.5079321 , 1.47890849, 1.01200703, 1.01200703])), Solution(fitness=1.0642211689959484, genome=array([0.51426029, 0.07739827, 0.15574055, 0.27336348, 0.32036201,
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+       0.04675171, 0.00573519, 0.19195522, 0.31812797, 0.43961492,
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+       1.25863917, 1.16332713, 1.13527431, 1.10028312, 1.10028312]))]"
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+       0.00808047, 0.21683189, 0.26892205, 0.43079915, 0.53290447,
+       0.57194062, 0.73892463, 0.8002086 , 0.99294915]), scaled_genome=None, log_likelihood=8796.057723430773, fitness_values=array([14.88430826,  3.5644366 ,  3.41501313,  3.41501313,  2.56110519,
+        2.30800763,  2.30800763,  1.83229451,  1.64263065,  1.64263065])), Solution(fitness=1.6232253020672247, genome=array([0.2155861 , 0.25482841, 0.14886257, 0.30417364, 0.21263362,
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+       0.48610513, 0.71999009, 0.86707593, 0.99153327]), scaled_genome=None, log_likelihood=12404.572645618351, fitness_values=array([9.79312529, 6.51348128, 3.66767346, 3.6512739 , 2.35217448,
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+        2.21805102,  1.50987982,  1.50987982,  1.50987982,  1.19726972])), Solution(fitness=1.196684626700488, genome=array([0.13254527, 0.20031085, 0.14519648, 0.03      , 0.15199712,
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+       0.66016559, 0.697556  , 0.82888526, 0.9967285 ]), scaled_genome=None, log_likelihood=11735.49869870371, fitness_values=array([14.4832829 ,  2.89046553,  2.89046553,  2.53471521,  2.24854358,
+        1.95349515,  1.46618668,  1.46618668,  1.39453424,  1.147548  ]))]"
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+       0.00656135, 0.18301484, 0.27763499, 0.40466707, 0.50649825,
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+       0.69734984, 0.61373131, 0.46509589, 0.44339847, 0.37067858])), Solution(fitness=0.4165070192673732, genome=array([0.135106  , 0.11487807, 0.03110863, 0.03634229, 0.0380466 ,
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+        1.00081604,  0.72289147,  0.47140878,  0.34544871,  0.30368258]))]"
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+       0.04190818, 0.03613024, 0.18542741, 0.36175522, 0.5349919 ,
+       0.62753233, 0.7431676 , 0.84953008, 0.99027054]), scaled_genome=None, log_likelihood=3131.01887540113, fitness_values=array([1.524639  , 0.9954786 , 0.69617702, 0.69617702, 0.53748492,
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+       0.03414534, 0.03453587, 0.1798324 , 0.35659932, 0.52480644,
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+       0.04182376, 0.02962167, 0.19222247, 0.35789483, 0.53442134,
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+       0.39374543, 0.36906082, 0.33618648, 0.30918793, 0.28128231])), Solution(fitness=0.2280608336246449, genome=array([0.47656915, 0.14528379, 0.04209482, 0.0307982 , 0.0821854 ,
+       0.03061525, 0.15727128, 0.1698906 , 0.02951654, 0.01289449,
+       0.00131643, 0.01688589, 0.0044224 , 0.01542495, 0.02369809,
+       0.03073578, 0.13853758, 0.25446537, 0.29009057, 0.43005968,
+       0.43068517, 0.56914614, 0.62419132, 0.67666912]), scaled_genome=None, log_likelihood=9044.690782059733, fitness_values=array([1.35766746, 0.93463263, 0.69876355, 0.60732544, 0.46885559,
+       0.43829187, 0.37828439, 0.31716041, 0.25535553, 0.22806083])), Solution(fitness=0.29587250199363296, genome=array([0.43128313, 0.0369524 , 0.21225103, 0.03040161, 0.08938227,
+       0.0580563 , 0.15638394, 0.14765082, 0.02275758, 0.0178236 ,
+       0.01856051, 0.03272255, 0.00489819, 0.02452083, 0.02230765,
+       0.03198297, 0.13010097, 0.20390691, 0.26721817, 0.32000017,
+       0.43105894, 0.58540189, 0.63492519, 0.68142394]), scaled_genome=None, log_likelihood=8878.76755182209, fitness_values=array([1.23395117, 0.95351795, 0.85032882, 0.66145542, 0.55419449,
+       0.44580065, 0.36713168, 0.32785435, 0.31033083, 0.2958725 ])), Solution(fitness=0.32399023182538345, genome=array([0.38777116, 0.10407774, 0.21244889, 0.03      , 0.0808349 ,
+       0.06114521, 0.17076589, 0.12114194, 0.02146692, 0.02717785,
+       0.01915051, 0.04558613, 0.00481453, 0.02763135, 0.02392525,
+       0.02971769, 0.1261977 , 0.17838605, 0.26837   , 0.32370939,
+       0.43098939, 0.58525529, 0.63808955, 0.68651081]), scaled_genome=None, log_likelihood=8766.533986794875, fitness_values=array([1.29963046, 1.08039181, 0.59656914, 0.59656914, 0.47906749,
+       0.45489   , 0.40656905, 0.36062074, 0.33582275, 0.32399023])), Solution(fitness=0.2945438599508195, genome=array([0.43162126, 0.03145429, 0.22993222, 0.03000023, 0.09761781,
+       0.08264442, 0.19897353, 0.07776051, 0.02324538, 0.01069139,
+       0.01924006, 0.02468547, 0.0048262 , 0.027183  , 0.0246624 ,
+       0.02495828, 0.13125486, 0.20421765, 0.2674244 , 0.41318897,
+       0.43137832, 0.59696677, 0.64546984, 0.69903459]), scaled_genome=None, log_likelihood=9042.430645511904, fitness_values=array([1.23261466, 0.78852523, 0.7077123 , 0.56018543, 0.45307095,
+       0.40729097, 0.35327232, 0.32161344, 0.30431414, 0.29454386])), Solution(fitness=0.2826653737027907, genome=array([0.43231416, 0.03120857, 0.22289239, 0.03029278, 0.07600334,
+       0.03785558, 0.15172113, 0.16567246, 0.02274476, 0.01256617,
+       0.01989783, 0.01488314, 0.00443642, 0.020212  , 0.02338933,
+       0.03108441, 0.13022495, 0.20419119, 0.26751174, 0.43492636,
+       0.43064331, 0.5776043 , 0.62796693, 0.67878058]), scaled_genome=None, log_likelihood=8877.425131459755, fitness_values=array([1.43057576, 0.90246962, 0.87211516, 0.65272134, 0.48804118,
+       0.42689301, 0.3612048 , 0.3334632 , 0.30306302, 0.28266537])), Solution(fitness=0.28480150019677264, genome=array([0.42277929, 0.03026366, 0.2250183 , 0.03018615, 0.08219204,
+       0.04576901, 0.18622953, 0.11069574, 0.02399008, 0.01177583,
+       0.01860755, 0.00612237, 0.00438429, 0.02034272, 0.02517211,
+       0.03211397, 0.13131055, 0.20551097, 0.26898907, 0.43633046,
+       0.43127232, 0.58188309, 0.63918454, 0.68680937]), scaled_genome=None, log_likelihood=8726.779710833725, fitness_values=array([0.86181133, 0.70684732, 0.6949385 , 0.51838045, 0.49298312,
+       0.43856428, 0.38284417, 0.33040307, 0.30675262, 0.2848015 ])), Solution(fitness=0.29317802619687505, genome=array([0.40151978, 0.07973016, 0.19267046, 0.03017511, 0.07966084,
+       0.03833325, 0.17189585, 0.13032735, 0.02277493, 0.03079359,
+       0.01814638, 0.00947329, 0.0042685 , 0.02192104, 0.02551339,
+       0.02997   , 0.1302858 , 0.21952632, 0.27073853, 0.43576728,
+       0.4308507 , 0.57986129, 0.63409215, 0.68417584]), scaled_genome=None, log_likelihood=8674.18728933101, fitness_values=array([1.36329039, 1.03998217, 0.84999855, 0.65667033, 0.53217692,
+       0.44599498, 0.35782195, 0.32742489, 0.30655272, 0.29317803]))]"
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+       2.69252273e-02, 9.81349740e-03, 3.42987020e-02, 7.62207079e-04,
+       8.86292105e-03, 3.04149739e-02, 4.02955488e-02, 3.26339391e-02,
+       1.15563931e-01, 1.41462650e-01, 2.66465120e-01, 3.65043028e-01,
+       4.34357258e-01, 5.57045182e-01, 6.56226295e-01, 7.62377461e-01]), scaled_genome=None, log_likelihood=6822.597260646963, fitness_values=array([1.17006921, 1.17006921, 1.17006921, 1.17006921, 1.06241934,
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+       0.03217406, 0.0089311 , 0.01329051, 0.01572651, 0.02914023,
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+       0.03      , 0.23623482, 0.10440182, 0.0346978 , 0.02505113,
+       0.01924508, 0.03009555, 0.01126743, 0.03342937, 0.01929794,
+       0.04227858, 0.10319212, 0.17194272, 0.2680565 , 0.31975976,
+       0.43149534, 0.54601036, 0.62796024, 0.68954875]), scaled_genome=None, log_likelihood=7926.463556439425, fitness_values=array([1.55033472, 1.45110128, 1.10762061, 1.10762061, 0.99202206,
+       0.99202206, 0.91101286, 0.91101286, 0.87958552, 0.87958552])), Solution(fitness=1.0111089556572666, genome=array([0.16139186, 0.1745487 , 0.19726346, 0.25216205, 0.19147041,
+       0.05702299, 0.18281289, 0.29755403, 0.02687146, 0.01243991,
+       0.03041607, 0.03105471, 0.03381443, 0.04510816, 0.01759634,
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+       0.02503963, 0.13290777, 0.30734134, 0.27751943, 0.43022032,
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+       0.99481359, 0.9861    , 0.9861    , 0.9861    , 0.91911335])), Solution(fitness=0.933857321628806, genome=array([0.34158072, 0.17612677, 0.05367655, 0.03      , 0.21951582,
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+       0.03      , 0.18966588, 0.20303884, 0.02955571, 0.0068471 ,
+       0.0527337 , 0.00537572, 0.01244529, 0.01347812, 0.02167254,
+       0.05219831, 0.10094163, 0.14142021, 0.24101536, 0.37536265,
+       0.43583555, 0.52216438, 0.60181711, 0.70036844]), scaled_genome=None, log_likelihood=8642.619912665414, fitness_values=array([1.24972773, 1.24972773, 1.24972773, 1.21454272, 1.21454272,
+       1.09169062, 1.09169062, 1.08322661, 0.96048629, 0.96048629])), Solution(fitness=0.9940472829316632, genome=array([0.31036725, 0.08684351, 0.12449108, 0.05728713, 0.27396052,
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+       0.04174521, 0.13209574, 0.2387731 , 0.28256969, 0.31837514,
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+       0.02230134, 0.00707029, 0.02135712, 0.0671424 , 0.02438087,
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+       1.00106665, 1.00106665, 0.928849  , 0.928849  , 0.928849  ])), Solution(fitness=0.6561727626074506, genome=array([0.17382828, 0.27910413, 0.14079065, 0.0607054 , 0.14581746,
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+       0.02690767, 0.1247187 , 0.17671438, 0.26961589, 0.38997654,
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+       1.14550489, 1.01742535, 1.01742535, 0.65617276, 0.65617276]))]"
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+       0.02318627, 0.13150521, 0.24521739, 0.28232062, 0.37604329,
+       0.43142098, 0.55737962, 0.6384847 , 0.70075394]), scaled_genome=None, log_likelihood=9003.308439278568, fitness_values=array([1.47469362, 1.05538976, 0.83902982, 0.71588836, 0.65763079,
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+       0.02770698, 0.12724538, 0.17861468, 0.27087083, 0.39840712,
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+       0.50226364, 0.50226364, 0.48137   , 0.43438004, 0.38070649])), Solution(fitness=0.38797670624325803, genome=array([0.38088739, 0.10367174, 0.17861013, 0.03      , 0.10850231,
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+       0.03027457, 0.12871699, 0.20321717, 0.27289666, 0.38014602,
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+       0.03126918, 0.13636803, 0.24060597, 0.28137445, 0.3779588 ,
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+       0.54944608, 0.47065831, 0.43346033, 0.40928503, 0.3927392 ])), Solution(fitness=0.3656769930582417, genome=array([0.469479  , 0.13876208, 0.14681781, 0.03175558, 0.0853557 ,
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+       0.01753608, 0.01551202, 0.00716809, 0.01458855, 0.02687583,
+       0.03028333, 0.13269222, 0.24552661, 0.28172027, 0.37054677,
+       0.43015525, 0.5660434 , 0.63157183, 0.69737676]), scaled_genome=None, log_likelihood=8952.954466643641, fitness_values=array([1.70422489, 1.15032841, 0.78900296, 0.67109307, 0.61140304,
+       0.47419171, 0.43205182, 0.42640049, 0.40421981, 0.36567699])), Solution(fitness=0.4025287080231713, genome=array([0.37539164, 0.07253724, 0.23352105, 0.05264348, 0.09899663,
+       0.042659  , 0.24600127, 0.13179342, 0.0241131 , 0.02113426,
+       0.0191054 , 0.0191304 , 0.0040755 , 0.02969556, 0.02761417,
+       0.0303957 , 0.13317686, 0.20100594, 0.26822587, 0.35381136,
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+       0.03      , 0.18711532, 0.13136498, 0.02431226, 0.01733027,
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+       0.02619748, 0.12732752, 0.20289427, 0.27048887, 0.3897673 ,
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+       0.53804733, 0.48311706, 0.43486561, 0.40230053, 0.36003997])), Solution(fitness=0.3821931537396574, genome=array([0.38658745, 0.10059674, 0.16276099, 0.03041074, 0.10043957,
+       0.07047018, 0.13101341, 0.17022508, 0.02180728, 0.03272986,
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+       0.794549  , 0.794549  , 0.794549  , 0.77340744, 0.64093765])), Solution(fitness=0.6669636903176537, genome=array([ 0.27728297,  0.10864271,  0.09213978,  0.11433629,  0.13749875,
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+        0.52235832,  0.65576234,  0.83618623,  1.0147713 ]), scaled_genome=None, log_likelihood=2323.677432115905, fitness_values=array([2.97853288, 1.0003432 , 1.0003432 , 1.0003432 , 1.0003432 ,
+       0.83974318, 0.67386283, 0.66696369, 0.66696369, 0.66696369]))]"
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+       0.56890005, 0.54424713, 0.53813586, 0.53287218, 0.53151127]))]"
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+        0.51357546,  0.66637779,  0.85628952,  0.93791837]), scaled_genome=None, log_likelihood=5983.436062637864, fitness_values=array([1.78347669, 1.15723394, 0.89506262, 0.63550384, 0.51494405,
+       0.44042552, 0.39096521, 0.33106642, 0.26626775, 0.24671991])), Solution(fitness=0.24668095236224788, genome=array([ 4.36859581e-02,  5.33832754e-02,  3.81981909e-02,  1.46210617e-01,
+        1.36866829e-01,  3.00051180e-01,  2.14696692e-01,  3.21020163e-01,
+        4.09629541e-02,  4.45935777e-02,  4.18682592e-02,  2.39443399e-02,
+        3.92383468e-02,  2.67623381e-02,  1.43891388e-02,  2.37823631e-02,
+       -4.23606540e-04,  1.28264918e-01,  2.78028300e-01,  4.51690214e-01,
+        5.14308265e-01,  6.58922077e-01,  8.55014380e-01,  9.38137435e-01]), scaled_genome=None, log_likelihood=6452.804826194299, fitness_values=array([1.70857063, 1.21300458, 0.77945654, 0.63632358, 0.57624142,
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+       0.01625467, 0.0235889 , 0.02952389, 0.02902257, 0.01336789,
+       0.02262753, 0.01088945, 0.1443587 , 0.2500515 , 0.45927239,
+       0.52033448, 0.65618725, 0.8570065 , 0.94266664]), scaled_genome=None, log_likelihood=5949.651136372303, fitness_values=array([1.91096058, 1.28211728, 0.78767774, 0.55211336, 0.52553164,
+       0.42286364, 0.40938819, 0.32313916, 0.29150428, 0.24787855])), Solution(fitness=0.24006618640828828, genome=array([0.06900622, 0.03      , 0.05235847, 0.15740433, 0.10486898,
+       0.25137065, 0.19794388, 0.25120871, 0.05568188, 0.04395565,
+       0.03355914, 0.01847859, 0.03013396, 0.03124105, 0.0149539 ,
+       0.02264002, 0.01428585, 0.13010084, 0.27255536, 0.45513836,
+       0.52855308, 0.66310863, 0.85658137, 0.93293057]), scaled_genome=None, log_likelihood=5519.474675035709, fitness_values=array([1.28743987, 0.86883131, 0.68959376, 0.58437789, 0.45233619,
+       0.37458339, 0.3248563 , 0.29710742, 0.24818998, 0.24006619])), Solution(fitness=0.25051415948822114, genome=array([0.05725898, 0.05832651, 0.03      , 0.17262246, 0.12524112,
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+       0.02253917, 0.00401073, 0.13092491, 0.27250268, 0.45421727,
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+        0.03386906,  0.02160708,  0.03817829,  0.03042156,  0.01652602,
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+        0.51961062,  0.66234726,  0.8582303 ,  0.94192255]), scaled_genome=None, log_likelihood=5611.07755707779, fitness_values=array([1.80880803, 1.40144269, 0.84931295, 0.71197402, 0.53524559,
+       0.45617671, 0.39088394, 0.30306848, 0.2924279 , 0.24961812])), Solution(fitness=0.2242212730104469, genome=array([0.08736447, 0.03      , 0.03      , 0.14467916, 0.12647668,
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+       0.03295024, 0.01789695, 0.03747684, 0.02801233, 0.01359403,
+       0.02405724, 0.00549566, 0.11977603, 0.25283656, 0.45618565,
+       0.52270372, 0.66782997, 0.85577961, 0.93766592]), scaled_genome=None, log_likelihood=6033.171818163551, fitness_values=array([1.69568471, 1.14512605, 0.78491765, 0.61401574, 0.52015369,
+       0.42455651, 0.34239276, 0.30347544, 0.27638424, 0.22422127])), Solution(fitness=0.2399770874268868, genome=array([ 0.0384441 ,  0.03      ,  0.04355162,  0.12429234,  0.16261733,
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+        0.0325261 ,  0.02583839,  0.05543346,  0.02640346,  0.01331936,
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+        0.52571444,  0.6704958 ,  0.85571815,  0.93812416]), scaled_genome=None, log_likelihood=5608.64576844713, fitness_values=array([1.96008154, 0.91688588, 0.75840107, 0.6365031 , 0.57389327,
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+       0.37176274, 0.37176274, 0.32658596, 0.29151765, 0.24664797]))]"
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+        0.05249128,  0.01795795,  0.02864174,  0.02747131,  0.01402145,
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+       0.50495184, 0.39603985, 0.2676237 , 0.19681093, 0.18156473]))]"
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+       0.26571563, 0.17064838, 0.12382209, 0.096215  , 0.08028915]))]"
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+       0.00193603, 0.09028521, 0.16871542, 0.37473297, 0.48785297,
+       0.63396043, 0.74500745, 0.8711057 , 0.94017802]), scaled_genome=None, log_likelihood=8014.4502705400755, fitness_values=array([3.30548863, 2.52438647, 1.56538597, 1.00580315, 0.76751491,
+       0.5563274 , 0.44428349, 0.31792822, 0.31792822, 0.26111633])), Solution(fitness=0.21212923555960625, genome=array([0.29504382, 0.1842167 , 0.04913314, 0.04333415, 0.12103322,
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+       0.00204116, 0.08854741, 0.15694386, 0.37921191, 0.44708461,
+       0.62410723, 0.72901147, 0.87403044, 0.93986662]), scaled_genome=None, log_likelihood=7885.46825215289, fitness_values=array([2.57741234, 2.21130362, 1.26347653, 0.9886857 , 0.66706707,
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+       0.00256413, 0.09045767, 0.16061526, 0.35250502, 0.46768575,
+       0.63766735, 0.76723036, 0.87567832, 0.93899851]), scaled_genome=None, log_likelihood=7128.846270287129, fitness_values=array([2.83209222, 1.61440658, 1.22862048, 1.17779482, 0.90541174,
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+       0.0023574 , 0.08673687, 0.145917  , 0.35536222, 0.47283638,
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+       0.48127525, 0.28875405, 0.28875405, 0.26793112, 0.24179358])), Solution(fitness=0.19209933645471708, genome=array([0.19790116, 0.20594409, 0.06466589, 0.03356399, 0.13084504,
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+       0.61904635, 0.74554611, 0.85097043, 0.94021798]), scaled_genome=None, log_likelihood=7211.301635743732, fitness_values=array([3.77184073, 1.96432318, 1.41437522, 0.86302735, 0.71569801,
+       0.54158362, 0.46372864, 0.37160007, 0.36625863, 0.19209934]))]"
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+       0.58694169, 0.61282733, 0.74324249, 0.89182006]), scaled_genome=None, log_likelihood=4876.427701822989, fitness_values=array([1.10899434, 0.67792259, 0.41810339, 0.18138901, 0.18138901,
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+       0.02816135, 0.06394078, 0.10479416, 0.20340895, 0.45884853,
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+       0.02981309, 0.07013638, 0.10526749, 0.22677273, 0.44706931,
+       0.57447174, 0.612342  , 0.74373567, 0.89234269]), scaled_genome=None, log_likelihood=4810.724177083835, fitness_values=array([1.14630675, 0.72480354, 0.45899888, 0.36273355, 0.25980029,
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+       0.02809935, 0.07056519, 0.10508181, 0.21234088, 0.45773633,
+       0.58387883, 0.6126632 , 0.74347656, 0.8943842 ]), scaled_genome=None, log_likelihood=4930.129740967217, fitness_values=array([1.33429318, 0.59000533, 0.43853521, 0.34271768, 0.24290629,
+       0.16133063, 0.09969594, 0.05783216, 0.02694154, 0.01783094]))]"
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+       0.18616382, 0.18175096, 0.1191459 , 0.02955893, 0.02615924,
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+       0.34299515, 0.2832395 , 0.25842757, 0.20582172, 0.17939269]))]"
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+ 0.19476663 0.2341635  0.23661075 0.21896277]",0.0,"[Solution(fitness=0.23976410938683038, genome=array([0.10058499, 0.17347434, 0.07443622, 0.06911057, 0.05762903,
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+       0.03332904, 0.02256158, 0.12034887, 0.26642873, 0.3770245 ,
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+       0.97535145, 0.83829912, 0.6881537 , 0.6881537 , 0.6881537 ])), Solution(fitness=0.6358336738123714, genome=array([ 0.05952356,  0.14237729,  0.17157728,  0.4284149 ,  0.14912175,
+        0.30595885,  0.04485495,  0.10276024,  0.05831488,  0.02151952,
+        0.04490966,  0.03604272,  0.04791937,  0.03663465,  0.03646426,
+        0.02844575, -0.10687966,  0.11928406,  0.37621912,  0.49226614,
+        0.69574196,  0.81542246,  0.98518441,  1.12856591]), scaled_genome=None, log_likelihood=3541.959810470809, fitness_values=array([2.11551764, 2.09123689, 1.17007533, 0.97631364, 0.97631364,
+       0.63583367, 0.63583367, 0.63583367, 0.63583367, 0.63583367])), Solution(fitness=0.6301292733712126, genome=array([ 0.131357  ,  0.08481061,  0.03      ,  0.26460303,  0.27531484,
+        0.41117469,  0.1285856 ,  0.03      ,  0.04335748,  0.02805192,
+        0.07817801,  0.03319404,  0.07795058,  0.0700622 ,  0.09620711,
+        0.09419134, -0.13188754,  0.1464129 ,  0.30540492,  0.46742937,
+        0.61318397,  0.77228081,  0.99223445,  1.28640367]), scaled_genome=None, log_likelihood=2970.040345155696, fitness_values=array([1.5086256 , 1.14485873, 1.14485873, 0.95265409, 0.95265409,
+       0.95265409, 0.84405519, 0.76208873, 0.70284438, 0.63012927])), Solution(fitness=0.5740898142563494, genome=array([0.03611708, 0.18796132, 0.19372473, 0.39353488, 0.21607682,
+       0.26950131, 0.1208857 , 0.0939615 , 0.0268276 , 0.02703448,
+       0.0815824 , 0.03114189, 0.08796214, 0.03548989, 0.09610325,
+       0.02721608, 0.07329449, 0.15964632, 0.23953426, 0.45953815,
+       0.65923183, 0.78465499, 0.926479  , 1.32650266]), scaled_genome=None, log_likelihood=4051.104588496137, fitness_values=array([2.00611669, 1.51635656, 1.33879097, 0.97227878, 0.91042798,
+       0.69576876, 0.69576876, 0.62845087, 0.57408981, 0.57408981]))]"
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+        0.5589487 ,  0.8012725 ,  0.99288379,  1.14019863]), scaled_genome=None, log_likelihood=3321.7684707939616, fitness_values=array([1.4559623 , 1.05530145, 0.64265508, 0.60360533, 0.44455386,
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+       0.38972501, 0.33500714, 0.2862252 , 0.26732353, 0.24747233])), Solution(fitness=0.21463670524728398, genome=array([0.03      , 0.15421513, 0.06999689, 0.30531761, 0.19807399,
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+       0.66706089, 0.81089551, 0.97630369, 1.14091806]), scaled_genome=None, log_likelihood=3809.1582240358275, fitness_values=array([1.29622886, 1.09567144, 0.92724771, 0.65551912, 0.49100368,
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+        0.0539506 ,  0.02608779,  0.07950303,  0.04639569,  0.05307656,
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+       0.33734919, 0.30674388, 0.2945372 , 0.24629389, 0.22497281])), Solution(fitness=0.2440959927686451, genome=array([0.03      , 0.16876405, 0.03662023, 0.38045692, 0.17287564,
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+       0.42833103, 0.33161423, 0.28939237, 0.26575934, 0.24409599]))]"
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+        0.09569447,  0.0312848 ,  0.04620147,  0.05313152,  0.08071209,
+        0.11079054, -0.06816474,  0.13715276,  0.32173156,  0.46169344,
+        0.62342175,  0.80564305,  1.00491406,  1.17604534]), scaled_genome=None, log_likelihood=2676.4861951884222, fitness_values=array([0.6396166 , 0.4590399 , 0.41058476, 0.32798113, 0.31168792,
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+        0.10944774, -0.09213253,  0.13988741,  0.31033295,  0.46387305,
+        0.62102035,  0.794654  ,  0.97143638,  1.18306569]), scaled_genome=None, log_likelihood=2615.9980663595943, fitness_values=array([0.48342548, 0.38624954, 0.34575861, 0.278682  , 0.25601544,
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+       0.07548494, 0.04059867, 0.14109262, 0.33933229, 0.46280019,
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+       0.3514052 , 0.32302205, 0.2854805 , 0.25485803, 0.2429945 ])), Solution(fitness=0.2422530185241217, genome=array([0.11177227, 0.07821372, 0.32735653, 0.06003838, 0.2877891 ,
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+       0.32255421, 0.05561227, 0.03110478, 0.09803027, 0.0134598 ,
+       0.04395812, 0.0253837 , 0.09003727, 0.0479249 , 0.06056909,
+       0.0590175 , 0.1049218 , 0.13565236, 0.37874856, 0.46554147,
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+       0.32743566, 0.28401433, 0.22395668, 0.17882849, 0.16106354])), Solution(fitness=0.06261112838079004, genome=array([0.03306981, 0.12232252, 0.14293976, 0.15233613, 0.13091984,
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+       0.40558136, 0.32448397, 0.22545496, 0.17064774, 0.14622433]))]"
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+       0.02212986, 0.0384887 , 0.21955768, 0.33702249, 0.49910184,
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+        6.61935309e-01,  7.92940903e-01,  8.34580458e-01,  9.69081313e-01]), scaled_genome=None, log_likelihood=6293.3071757818625, fitness_values=array([4.23002272, 2.19104474, 1.54863894, 0.7873777 , 0.55593305,
+       0.3015237 , 0.19174611, 0.13543331, 0.09691487, 0.07193307])), Solution(fitness=0.06774083276378681, genome=array([1.25745089e-01, 3.00718873e-02, 3.00401539e-02, 8.18743600e-02,
+       1.30987690e-01, 1.69987784e-01, 1.60552766e-01, 3.13354861e-01,
+       2.72849191e-02, 6.37431841e-03, 4.09928204e-02, 1.73794879e-02,
+       2.65367417e-02, 1.97010705e-02, 1.37861931e-03, 7.11169950e-02,
+       4.55044909e-05, 2.63562687e-01, 4.65934949e-01, 5.04158083e-01,
+       6.64368203e-01, 7.78772557e-01, 8.34584021e-01, 9.52524444e-01]), scaled_genome=None, log_likelihood=6150.253645575425, fitness_values=array([1.86948744, 1.38144139, 0.9609442 , 0.45742552, 0.39436387,
+       0.26527083, 0.15774407, 0.10950416, 0.07826442, 0.06774083])), Solution(fitness=0.06510384683759904, genome=array([ 0.1391384 ,  0.0300233 ,  0.03058907,  0.09467006,  0.13208586,
+        0.17797971,  0.16403237,  0.30251953,  0.02688634,  0.05605767,
+        0.0399699 ,  0.01873202,  0.02625972,  0.02071318,  0.00141856,
+        0.06986298, -0.0038606 ,  0.06295875,  0.46195175,  0.50500374,
+        0.66235376,  0.77846738,  0.8345744 ,  0.95255658]), scaled_genome=None, log_likelihood=6474.187199541623, fitness_values=array([2.63578791, 1.41545701, 1.05765847, 0.68351008, 0.46095302,
+       0.32869775, 0.18535057, 0.12557991, 0.08801917, 0.06510385])), Solution(fitness=0.06910192919796597, genome=array([ 1.24895531e-01,  3.00315690e-02,  3.01697857e-02,  8.89582699e-02,
+        1.35249162e-01,  1.69712515e-01,  1.63203296e-01,  3.15057030e-01,
+        2.56454625e-02,  3.46318383e-02,  4.07611111e-02,  1.66441794e-02,
+        2.64393203e-02,  2.00624414e-02,  1.41358664e-03,  7.01003578e-02,
+       -3.84123074e-04,  2.40996360e-01,  4.62341074e-01,  5.05382363e-01,
+        6.63305856e-01,  7.79267089e-01,  8.34576143e-01,  9.51885772e-01]), scaled_genome=None, log_likelihood=6292.193181181503, fitness_values=array([1.77929928, 1.77929928, 0.97227374, 0.64642457, 0.34632256,
+       0.26650771, 0.1574532 , 0.09699472, 0.07922165, 0.06910193])), Solution(fitness=0.06727541567677314, genome=array([ 0.10086343,  0.0303845 ,  0.03574222,  0.08118864,  0.12212537,
+        0.23828095,  0.15413668,  0.27958287,  0.0216737 ,  0.01399267,
+        0.07798714,  0.01721455,  0.02349238,  0.03390113,  0.00124087,
+        0.06246053, -0.00783299,  0.03046114,  0.46429252,  0.50276696,
+        0.66357703,  0.78849078,  0.83460611,  0.96378533]), scaled_genome=None, log_likelihood=6336.724765747135, fitness_values=array([2.13528893, 2.13192092, 1.14541814, 0.54256909, 0.33526966,
+       0.21015262, 0.1534866 , 0.10491203, 0.08036167, 0.06727542])), Solution(fitness=0.056385338534882916, genome=array([ 0.09409133,  0.04044221,  0.03936313,  0.08037671,  0.12962927,
+        0.18174471,  0.1567176 ,  0.32604283,  0.02301167,  0.02334406,
+        0.06793259,  0.01836464,  0.02509158,  0.01989183,  0.00135714,
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+        0.66320909,  0.77895341,  0.83463734,  0.95434363]), scaled_genome=None, log_likelihood=6445.639850471189, fitness_values=array([2.5973567 , 1.84565843, 1.2960838 , 0.59127727, 0.35771468,
+       0.31030338, 0.16725378, 0.12535779, 0.07401529, 0.05638534])), Solution(fitness=0.07522359561683038, genome=array([ 0.10968508,  0.03199608,  0.03014286,  0.09030976,  0.11426202,
+        0.27277195,  0.16022534,  0.24583962,  0.02216898,  0.10255064,
+        0.05122917,  0.01868362,  0.02580781,  0.04068863,  0.00129315,
+        0.05300026, -0.00148815,  0.03248013,  0.45280598,  0.50401582,
+        0.66245942,  0.79419653,  0.83460126,  0.97403519]), scaled_genome=None, log_likelihood=6318.948954569466, fitness_values=array([2.14727475, 1.45334774, 0.92337557, 0.53333936, 0.37601601,
+       0.26224621, 0.16760172, 0.13504339, 0.1011591 , 0.0752236 ]))]"
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+        0.62168615,  0.73042211,  0.8406458 ,  1.01368709]), scaled_genome=None, log_likelihood=5470.880335368637, fitness_values=array([3.88246623, 3.19851354, 2.09141317, 2.09141317, 2.09141317,
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+        2.44308413e-02,  4.32056689e-02,  3.72549198e-02,  2.73616901e-02,
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+       0.04409905, 0.09726755, 0.30170762, 0.37974452, 0.46535456,
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+       0.0503975 , 0.09547107, 0.24207613, 0.31008302, 0.37861357,
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+       0.05247305, 0.10007608, 0.29999086, 0.37870691, 0.46641988,
+       0.54256704, 0.6568029 , 0.79871826, 0.91582035]), scaled_genome=None, log_likelihood=1708.7357679557906, fitness_values=array([0.83956157, 0.5216177 , 0.33410829, 0.22221192, 0.13672295,
+       0.09372357, 0.08193548, 0.06269728, 0.05701674, 0.05324479])), Solution(fitness=0.052174771762677305, genome=array([0.16362757, 0.11443169, 0.15981014, 0.06255462, 0.06279884,
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+       0.10675099, 0.07150855, 0.06027264, 0.05327617, 0.05233248])), Solution(fitness=0.052061135758806575, genome=array([0.16486458, 0.11522675, 0.15302335, 0.06425366, 0.06498931,
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+       0.08777075, 0.06699615, 0.05632369, 0.05264827, 0.05206114]))]"
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+       0.43036476, 0.410336  , 0.410336  , 0.410336  , 0.410336  ])), Solution(fitness=0.4962013290032375, genome=array([0.2846149 , 0.12545022, 0.15942436, 0.07669184, 0.32403138,
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+       0.21290779, 0.17564699, 0.16846229, 0.13703243, 0.13149198])), Solution(fitness=0.08939447639014592, genome=array([0.13402988, 0.08468913, 0.2066247 , 0.08752388, 0.06936155,
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+       0.17467268, 0.17518426, 0.07333941, 0.03620287, 0.06720371,
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+       0.21134002, 0.16328649, 0.14338761, 0.11131039, 0.10175737]))]"
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+ 0.13421705 0.07898196 0.0763274  0.08904509]",0.0,"[Solution(fitness=0.08139671622129739, genome=array([0.12089704, 0.11619447, 0.16175903, 0.15520734, 0.1466799 ,
+       0.09474583, 0.10878332, 0.09573306, 0.04360312, 0.08935479,
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+       0.04524759, 0.08739731, 0.25177237, 0.37932236, 0.50988856,
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+       0.5680774 , 0.65113563, 0.86075397, 0.92397045]), scaled_genome=None, log_likelihood=2813.056479041182, fitness_values=array([1.42647417, 1.42647417, 1.42647417, 1.24642303, 1.24642303,
+       1.24642303, 1.24642303, 1.23576509, 1.23576509, 1.21277333])), Solution(fitness=1.104149992903088, genome=array([0.07277607, 0.1385041 , 0.29884   , 0.15943316, 0.2632604 ,
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+       0.03122987, 0.01874074, 0.06782957, 0.003132  , 0.02660054,
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+        0.03905079,  0.05145874,  0.02474881,  0.00987545,  0.02499761,
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+        0.57583366,  0.63528355,  0.77791617,  0.98110262]), scaled_genome=None, log_likelihood=4173.158976954666, fitness_values=array([2.89389018, 2.25881849, 1.9784799 , 1.42905986, 1.42905986,
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+        0.02148463,  0.03060613,  0.03470313,  0.01015528,  0.07619616,
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+       1.33752223, 1.33752223, 1.04879148, 1.04879148, 1.04879148]))]"
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+       0.19483136, 0.18384325, 0.20602074, 0.0291443 , 0.02779624,
+       0.02872039, 0.00514894, 0.0462027 , 0.03482132, 0.00910463,
+       0.03393847, 0.02955879, 0.17015174, 0.27709797, 0.40538903,
+       0.51278457, 0.65110492, 0.70984524, 0.93739111]), scaled_genome=None, log_likelihood=6116.747746283362, fitness_values=array([2.0982494 , 1.28528897, 0.80945807, 0.60618546, 0.51845315,
+       0.40593109, 0.30410485, 0.20141441, 0.17934185, 0.16695322])), Solution(fitness=0.18685195981771086, genome=array([0.14709023, 0.11139738, 0.03      , 0.20011574, 0.18159042,
+       0.24436008, 0.03      , 0.20261332, 0.02713097, 0.02428595,
+       0.04031392, 0.00363889, 0.06034439, 0.0233438 , 0.04750244,
+       0.02803287, 0.02692089, 0.17907968, 0.29496782, 0.4062224 ,
+       0.51488699, 0.69893213, 0.8033251 , 0.94216682]), scaled_genome=None, log_likelihood=5907.7377601195385, fitness_values=array([2.22496838, 1.01558915, 0.77582492, 0.57960664, 0.37222262,
+       0.33694417, 0.2552593 , 0.23939642, 0.2105667 , 0.18685196])), Solution(fitness=0.12470561053068037, genome=array([0.19432373, 0.11744457, 0.04739755, 0.2050281 , 0.07595604,
+       0.11632995, 0.13296653, 0.24286496, 0.03865405, 0.02602498,
+       0.04392073, 0.00535278, 0.04873825, 0.028967  , 0.01025296,
+       0.02646081, 0.02988217, 0.16622067, 0.27874839, 0.40354918,
+       0.51616847, 0.66492283, 0.70902057, 0.94169284]), scaled_genome=None, log_likelihood=6133.203136233132, fitness_values=array([1.75765312, 0.94987796, 0.77025486, 0.43087208, 0.42962318,
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+       0.03624485, 0.00552289, 0.05249577, 0.0303884 , 0.01003936,
+       0.03065766, 0.02000379, 0.17147764, 0.27899462, 0.40548985,
+       0.51937495, 0.68309589, 0.70633741, 0.93822381]), scaled_genome=None, log_likelihood=6369.764406691616, fitness_values=array([1.69867823, 1.08847193, 0.67909894, 0.38516379, 0.38516379,
+       0.35782869, 0.3199357 , 0.24548148, 0.159593  , 0.14214714])), Solution(fitness=0.09299198987337605, genome=array([0.18072891, 0.08862834, 0.0354674 , 0.22529795, 0.10852572,
+       0.11852166, 0.18097207, 0.22099212, 0.03073783, 0.02471862,
+       0.0441197 , 0.00336952, 0.06630243, 0.02702956, 0.00885742,
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+       0.04854408, 0.0042703 , 0.04534   , 0.03839575, 0.01205689,
+       0.02577478, 0.02445979, 0.16682893, 0.25678147, 0.40646688,
+       0.50029238, 0.6474905 , 0.70685684, 0.94110091]), scaled_genome=None, log_likelihood=6587.790644835109, fitness_values=array([1.57717544, 0.82673329, 0.77668737, 0.64058216, 0.36197946,
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+       0.5055419 , 0.69252296, 0.741812  , 0.93500273]), scaled_genome=None, log_likelihood=5848.9795161163875, fitness_values=array([1.27155024, 0.86862683, 0.7634168 , 0.67056684, 0.48257256,
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+       0.02852904, 0.02593747, 0.16993395, 0.27010219, 0.40532216,
+       0.50044347, 0.65816137, 0.70454696, 0.9364978 ]), scaled_genome=None, log_likelihood=6634.13087438218, fitness_values=array([1.9452176 , 1.11885285, 0.63581773, 0.47382287, 0.42837468,
+       0.25971485, 0.25924688, 0.22636832, 0.16921165, 0.13262206]))]"
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+       0.02686448, 0.02807753, 0.16484643, 0.29726793, 0.40752483,
+       0.5353496 , 0.69468356, 0.79171016, 0.9349686 ]), scaled_genome=None, log_likelihood=4716.216047800317, fitness_values=array([0.70632017, 0.48666716, 0.36014495, 0.28999059, 0.26865665,
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+       0.10318366, 0.15181807, 0.20831402, 0.02921925, 0.01639722,
+       0.09223352, 0.00463761, 0.05809507, 0.03820595, 0.01281198,
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+        0.01663102,  0.06716357,  0.00594811,  0.06144588,  0.03150906,
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+       0.13581883, 0.0687647 , 0.02853826, 0.01885266, 0.0152545 ])), Solution(fitness=0.014595328321120763, genome=array([0.21864614, 0.22277279, 0.03171508, 0.04425583, 0.05254584,
+       0.07216438, 0.33472056, 0.03246489, 0.05679474, 0.02694122,
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+       0.10704267, 0.06225742, 0.03291162, 0.01735428, 0.01459533]))]"
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+       0.04956586, 0.05420493, 0.19675911, 0.29084348, 0.34729009,
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+       0.06810864, 0.0500523 , 0.19396164, 0.34117863, 0.4342869 ,
+       0.58303177, 0.72133526, 0.84280831, 0.9292487 ]), scaled_genome=None, log_likelihood=5148.172995656397, fitness_values=array([4.79015768, 2.79641313, 2.79641313, 1.94174329, 1.27828086,
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+       0.0009327 , 0.03289085, 0.02370054, 0.0009327 , 0.04069846,
+       0.04080569, 0.0639072 , 0.1840006 , 0.32424462, 0.45189798,
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+       1.14754434, 0.85557007, 0.85557007, 0.85557007, 0.80239755]))]"
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+        0.04662974,  0.02094614,  0.00818224,  0.03586026,  0.04965367,
+        0.05606001, -0.00743071,  0.12710159,  0.23839024,  0.4494833 ,
+        0.55947686,  0.67481606,  0.82838582,  1.02836884]), scaled_genome=None, log_likelihood=7374.105312217493, fitness_values=array([4.59474616, 3.79036326, 1.90887922, 1.90887922, 1.90887922,
+       1.87086467, 1.75384907, 1.75384907, 1.41053868, 1.40579968])), Solution(fitness=0.7323245561316527, genome=array([0.07227888, 0.10880308, 0.18133407, 0.04547824, 0.17010708,
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+       0.55735815, 0.73412653, 0.82646937, 0.98061447]), scaled_genome=None, log_likelihood=7185.0006631736, fitness_values=array([3.53981102, 3.53981102, 3.38461144, 2.1078251 , 2.1078251 ,
+       1.84225085, 1.03451468, 1.03451468, 1.03451468, 0.73232456]))]"
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+       0.03285807, 0.05108922, 0.18665763, 0.303272  , 0.44529119,
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+       0.68654069, 0.46720107, 0.39103644, 0.30227464, 0.245986  ])), Solution(fitness=0.213301421262875, genome=array([0.04220614, 0.11159312, 0.058607  , 0.05880157, 0.31757663,
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+       0.0401216 , 0.05072926, 0.16667994, 0.28277475, 0.44910707,
+       0.55742952, 0.71212927, 0.83290771, 0.99609598]), scaled_genome=None, log_likelihood=9485.074889189871, fitness_values=array([3.50282852, 1.75131345, 1.6800682 , 0.97104744, 0.61979149,
+       0.53518448, 0.42122318, 0.3530715 , 0.25695184, 0.21330142])), Solution(fitness=0.23175748780010308, genome=array([0.05459623, 0.07768699, 0.06473608, 0.0616868 , 0.30480904,
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+        0.0398083 ,  0.04715055,  0.00197106,  0.05930783,  0.01997387,
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+        4.27772509e-02,  5.02203434e-02,  3.67818413e-02,  3.58808644e-02,
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+       0.42413108, 0.38187889, 0.3077469 , 0.27984732, 0.2626578 ])), Solution(fitness=0.2696687539437757, genome=array([0.03978746, 0.05910491, 0.05543472, 0.07157942, 0.27590854,
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+       0.46066149, 0.33426311, 0.33426311, 0.30986044, 0.26966875]))]"
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+       0.30682131, 0.28759231, 0.275941  , 0.26214226, 0.25688456])), Solution(fitness=0.23815187306506577, genome=array([ 0.03062564,  0.05171361,  0.06596415,  0.05876462,  0.2825003 ,
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+       0.16270883, 0.076715  , 0.13762455, 0.11551271, 0.06338225,
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+       0.25831039, 0.23417064, 0.22790529, 0.22243522, 0.21352541])), Solution(fitness=0.23669870125407214, genome=array([0.04544568, 0.0592611 , 0.10031223, 0.04056939, 0.27857082,
+       0.09769571, 0.15670485, 0.22144022, 0.07697072, 0.05177851,
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+       0.42633051, 0.41606701, 0.40781563, 0.24901347, 0.2366987 ])), Solution(fitness=0.40337972118493726, genome=array([0.03301254, 0.0530948 , 0.05592026, 0.04742305, 0.30494095,
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+       0.45667336, 0.44301534, 0.4301157 , 0.4167405 , 0.40337972])), Solution(fitness=0.472090871547743, genome=array([ 0.04202238,  0.05141295,  0.03108118,  0.04314535,  0.34224872,
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+       0.58021256, 0.56247016, 0.52811844, 0.52213041, 0.47209087]))]"
+4,synthetic_8_59,LSHADESolver,0.11597186007244753,0.00748467993214242,"[0.13221914 0.11279232 0.11077134 0.10475346 0.12252555 0.11416706
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+       0.46914285, 0.37558803, 0.23252653, 0.13453026, 0.11077134])), Solution(fitness=0.10475346080239589, genome=array([0.03276175, 0.07890519, 0.03864302, 0.05152444, 0.29683161,
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+       0.49433343, 0.40387914, 0.21946241, 0.13306985, 0.10475346])), Solution(fitness=0.1225255521319125, genome=array([0.03141081, 0.07323903, 0.03167438, 0.05956012, 0.30344938,
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+       0.05436887, 0.1129977 , 0.13123584, 0.08110327, 0.00600522,
+       0.04984904, 0.0659513 , 0.00296188, 0.06341227, 0.04299685,
+       0.04087669, 0.05410031, 0.25170758, 0.39174278, 0.52682266,
+       0.64974327, 0.72956834, 0.95865997, 1.03967739]), scaled_genome=None, log_likelihood=5831.384721289543, fitness_values=array([1.96400041, 1.37391049, 1.12729989, 0.84581281, 0.73215945,
+       0.60029244, 0.4811519 , 0.44923588, 0.38898721, 0.34989942])), Solution(fitness=0.3173394552679258, genome=array([0.11770125, 0.29976782, 0.11243159, 0.21225327, 0.20087944,
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+       0.0638664 , 0.05339627, 0.00308741, 0.04436229, 0.0498655 ,
+       0.04196663, 0.06904438, 0.25338442, 0.40679038, 0.5389138 ,
+       0.65043471, 0.71161088, 0.95011688, 1.04719977]), scaled_genome=None, log_likelihood=7011.5900129938655, fitness_values=array([2.66766054, 1.78195994, 1.31598557, 1.00759135, 0.69163516,
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+       0.04417876, 0.06967614, 0.25416693, 0.35660462, 0.51529589,
+       0.64758964, 0.72150083, 0.95363901, 1.04809923]), scaled_genome=None, log_likelihood=6732.014129732805, fitness_values=array([1.18819675, 1.08747787, 0.9993768 , 0.86491323, 0.73323462,
+       0.61986086, 0.51757   , 0.4384144 , 0.42141949, 0.36299714])), Solution(fitness=0.2947930763102231, genome=array([0.15582022, 0.28420685, 0.03221477, 0.16841137, 0.18003955,
+       0.09505942, 0.11846008, 0.15628698, 0.06653525, 0.0060957 ,
+       0.03858583, 0.05987833, 0.00821003, 0.0574177 , 0.0408325 ,
+       0.04096985, 0.08294052, 0.25242017, 0.34884742, 0.50017365,
+       0.64602902, 0.69262204, 0.93955139, 1.04331335]), scaled_genome=None, log_likelihood=6235.71888910923, fitness_values=array([1.4560272 , 1.11670112, 0.93089069, 0.7482501 , 0.60214938,
+       0.54289368, 0.44324788, 0.37695878, 0.35692894, 0.29479308])), Solution(fitness=0.3439859850378521, genome=array([0.13887721, 0.28020154, 0.08207158, 0.16892927, 0.16022594,
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+       0.04838761, 0.08966003, 0.25203342, 0.34698468, 0.5048448 ,
+       0.64781841, 0.69485374, 0.9512232 , 1.05405346]), scaled_genome=None, log_likelihood=5873.7739471812065, fitness_values=array([2.26879075, 1.83255355, 1.09995899, 0.93627067, 0.72004068,
+       0.66544821, 0.49994611, 0.4132452 , 0.4097866 , 0.34398599])), Solution(fitness=0.3344888717395606, genome=array([0.143001  , 0.2884247 , 0.05759754, 0.22779513, 0.25766195,
+       0.1128706 , 0.17669037, 0.10760457, 0.07151132, 0.00638616,
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+       0.04462512, 0.06304234, 0.25277483, 0.32606867, 0.5034054 ,
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+       0.64781377, 0.71751857, 0.97003674, 1.06170439]), scaled_genome=None, log_likelihood=6251.214376444652, fitness_values=array([2.23901414, 1.25353052, 1.25353052, 1.00243879, 0.71844781,
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+       0.0434951 , 0.07750462, 0.25296541, 0.38772048, 0.51299451,
+       0.64752846, 0.71321049, 0.93997245, 1.04108558]), scaled_genome=None, log_likelihood=6555.38658850066, fitness_values=array([3.03003672, 1.47476236, 1.18885043, 0.93093805, 0.81080101,
+       0.61589351, 0.50718695, 0.43579867, 0.41152462, 0.35966514]))]"
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+       0.04920856, 0.05814201, 0.25207741, 0.348774  , 0.49452611,
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+       0.05189192, 0.10088145, 0.19162274, 0.34008913, 0.4665942 ,
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+       0.0045697 , 0.10691773, 0.27970888, 0.37551505, 0.52820708,
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+       1.54731754, 1.18916587, 0.85334124, 0.68809294, 0.62809032]))]"
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+       0.03814256, 0.05434308, 0.01456169, 0.02182153, 0.06306112,
+       0.04697525, 0.02300788, 0.15352477, 0.29600784, 0.4469882 ,
+       0.5610134 , 0.63401656, 0.82473323, 0.93380448]), scaled_genome=None, log_likelihood=3765.108334988911, fitness_values=array([1.38263253, 1.10857989, 1.10857989, 1.05218524, 1.05218524,
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+       0.02523506, 0.0643587 , 0.13593069, 0.25688937, 0.35260634,
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+       0.01876164, 0.03522276, 0.01688415, 0.01565874, 0.03093068,
+       0.04507018, 0.04429166, 0.14125611, 0.24540325, 0.35552462,
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+        0.09486947,  0.12452294,  0.26569644,  0.039519  ,  0.01871608,
+        0.03021118,  0.06245194,  0.03068225,  0.0304946 ,  0.02136663,
+        0.02146348, -0.01779156,  0.13147257,  0.26790475,  0.40008504,
+        0.56916835,  0.7615911 ,  0.87310835,  0.95358752]), scaled_genome=None, log_likelihood=4093.377358805581, fitness_values=array([2.67600284, 1.74856097, 1.6302149 , 1.10363756, 1.04651283,
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+       1.13350595, 0.89038829, 0.89038829, 0.82461387, 0.82461387]))]"
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+       0.55962147, 0.66711251, 0.823118  , 0.9094786 ]), scaled_genome=None, log_likelihood=4688.23912496656, fitness_values=array([1.302746  , 0.96955919, 0.77262269, 0.65633685, 0.57316176,
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+       0.4840097 , 0.40767456, 0.37128876, 0.321513  , 0.31484501])), Solution(fitness=0.34224241942642303, genome=array([0.26511817, 0.18664798, 0.1073373 , 0.10470487, 0.18884475,
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+       0.73085537, 0.69250284, 0.612583  , 0.56357059, 0.50240615])), Solution(fitness=0.41867049266154643, genome=array([0.09318703, 0.10487467, 0.18423819, 0.13614884, 0.12866104,
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+       0.69291753, 0.7652147 , 0.87790701, 0.96778943]), scaled_genome=None, log_likelihood=4293.243754233891, fitness_values=array([1.65998349, 1.07837749, 0.77795414, 0.6498894 , 0.62549982,
+       0.40496831, 0.37671017, 0.35413431, 0.27198901, 0.18789271])), Solution(fitness=0.27084502072260735, genome=array([0.14776723, 0.15312553, 0.16335325, 0.10613514, 0.15898257,
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+       0.03022088, 0.04568093, 0.00630215, 0.02533258, 0.03507721,
+       0.0131041 , 0.03437335, 0.22445508, 0.33658817, 0.54384607,
+       0.69237419, 0.76066019, 0.91938858, 0.979922  ]), scaled_genome=None, log_likelihood=3324.4202647014313, fitness_values=array([2.79173917, 1.35362612, 0.71243397, 0.71243397, 0.53814117,
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+       0.69077762, 0.75628044, 0.93288444, 0.987234  ]), scaled_genome=None, log_likelihood=3222.2537532730307, fitness_values=array([1.66191998, 0.83711395, 0.71629133, 0.68571805, 0.52246418,
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+       0.05201862, 0.05310147, 0.00570649, 0.03455029, 0.02038647,
+       0.07311578, 0.03705312, 0.20585912, 0.3302151 , 0.54311012,
+       0.69100739, 0.77551984, 0.95696095, 1.01267046]), scaled_genome=None, log_likelihood=3905.920748288182, fitness_values=array([1.38206027, 1.11847688, 0.7761696 , 0.60825386, 0.60825386,
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+       0.01888807, 0.01552404, 0.22488715, 0.33081999, 0.54615896,
+       0.6920248 , 0.76414259, 0.89555931, 0.97223439]), scaled_genome=None, log_likelihood=3012.2337379482574, fitness_values=array([2.29272759, 1.12522382, 0.9456349 , 0.64885707, 0.48625675,
+       0.46638161, 0.37810056, 0.28718267, 0.28718267, 0.27400337])), Solution(fitness=0.1340932137302528, genome=array([0.16877714, 0.11657088, 0.17454319, 0.16256053, 0.16157707,
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+       0.00772122, 0.02939587, 0.20655484, 0.32586853, 0.54201944,
+       0.69247647, 0.77235074, 0.96244457, 1.04563656]), scaled_genome=None, log_likelihood=4092.3782123512015, fitness_values=array([1.73634463, 0.99482723, 0.89740115, 0.63260788, 0.5388558 ,
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+       0.56020535, 0.3651768 , 0.27501822, 0.27501822, 0.2286112 ]))]"
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+       0.52617728, 0.48121672, 0.32828552, 0.1363864 , 0.04469232])), Solution(fitness=0.03750132385036144, genome=array([0.16099769, 0.11706431, 0.14201565, 0.13581938, 0.14302202,
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+       0.52179344, 0.52812761, 0.65382641, 0.72784002]), scaled_genome=None, log_likelihood=11845.636136791203, fitness_values=array([1.64644067, 1.00904425, 0.71876665, 0.49678227, 0.39892682,
+       0.35710936, 0.32776192, 0.299998  , 0.27344429, 0.20735303])), Solution(fitness=0.16115443032586654, genome=array([0.3364544 , 0.16029153, 0.05277154, 0.0389589 , 0.03922693,
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+       0.02414956, 0.02263508, 0.04349003, 0.00594474, 0.04055309,
+       0.00393163, 0.13340745, 0.22116867, 0.27577368, 0.31696424,
+       0.47633607, 0.52839446, 0.6520117 , 0.72800616]), scaled_genome=None, log_likelihood=11866.538247468961, fitness_values=array([2.37569833, 1.0332445 , 0.75732599, 0.52195913, 0.40051454,
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+       0.02906792, 0.04383958, 0.03428318, 0.00598415, 0.04643543,
+       0.00396115, 0.1308077 , 0.22096876, 0.28380509, 0.35845438,
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+       0.00363173, 0.12893224, 0.22096103, 0.27855788, 0.32104122,
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+       0.51495872, 0.52840222, 0.64841134, 0.727549  ]), scaled_genome=None, log_likelihood=11932.135590105876, fitness_values=array([2.11541311, 1.20108048, 0.81468362, 0.59633987, 0.44012886,
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+       0.00525892, 0.13338168, 0.2215661 , 0.29087335, 0.40880362,
+       0.51510675, 0.52828888, 0.64887925, 0.7277159 ]), scaled_genome=None, log_likelihood=11810.364432080376, fitness_values=array([2.03144449, 1.02970684, 0.75155406, 0.57302874, 0.4652032 ,
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+       0.00396726, 0.13318957, 0.2212532 , 0.2743841 , 0.31656636,
+       0.47737236, 0.52837174, 0.65405618, 0.7278587 ]), scaled_genome=None, log_likelihood=11885.92585470156, fitness_values=array([1.98064367, 0.86147273, 0.74601356, 0.45035435, 0.34239454,
+       0.25230549, 0.20106349, 0.17313802, 0.16233875, 0.15919965]))]"
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+       0.50002983, 0.4064624 , 0.4064624 , 0.34462952, 0.34462952]))]"
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+       0.04720918, 0.05181154, 0.26175361, 0.42618234, 0.51279635,
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+       0.0541243 , 0.03860638, 0.16196579, 0.40713353, 0.5167568 ,
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+       0.03591733, 0.05529207, 0.23905184, 0.41532196, 0.50801739,
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+       0.34915609, 0.27284549, 0.16769407, 0.09686631, 0.06647104]))]"
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+       0.30014727, 0.23844806, 0.16704534, 0.11142285, 0.0874877 ])), Solution(fitness=0.09931868798123633, genome=array([0.09081472, 0.06864993, 0.06836506, 0.032408  , 0.03      ,
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+       0.04214139, 0.0539281 , 0.11878245, 0.25427755, 0.23592842,
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+       0.02951904, 0.02937368, 0.02934581, 0.02933466, 0.02933224]))]"
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+       0.02992937, 0.02225975, 0.02184013, 0.02171794, 0.0217077 ]))]"
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+       0.91800215, 1.0470854 ]), scaled_genome=None, log_likelihood=2874.216457176114, fitness_values=array([0.49282578, 0.30785407, 0.27523916, 0.26725176, 0.26452188,
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+       0.04298899, 0.09024592, 0.09256179, 0.29854584, 0.52916724,
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+       0.04321672, 0.09132528, 0.09327354, 0.29862065, 0.5290445 ,
+       0.92080601, 1.04579825]), scaled_genome=None, log_likelihood=2886.560109376003, fitness_values=array([0.50323668, 0.33659413, 0.27984551, 0.26683946, 0.26462598,
+       0.26430419, 0.26423968, 0.26422244, 0.26419967, 0.26418937]))]"
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+       0.26567251, 0.26473356, 0.26446574, 0.26437937, 0.26429676]))]"
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+       0.29081525, 0.28817297, 0.28765844, 0.28691289, 0.28616   ])), Solution(fitness=0.285297476585332, genome=array([0.76627801, 0.06005684, 0.05406053, 0.11960463, 0.06360628,
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+       0.11242948, 0.10559007, 0.10490121, 0.1047113 , 0.1046797 ]))]"
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+       0.02325384, 0.01814971, 0.01689749, 0.01652146, 0.01644502]))]"
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+       0.0498679 , 0.03663869, 0.07576676, 0.02781295, 0.18330185,
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+       0.0552621 , 0.04947379, 0.04835396, 0.04821979, 0.04820626]))]"
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+       0.06467479, 0.03856043, 0.02720982, 0.02484264, 0.02309159]))]"
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+       0.05950329, 0.04773354, 0.04098915, 0.03246888, 0.03100267])), Solution(fitness=0.03931140233105799, genome=array([0.1295234 , 0.43831915, 0.38005037, 0.09491947, 0.09039846,
+       0.05095346, 0.0933238 , 0.01233301, 0.074038  , 0.25258689,
+       0.50907167, 0.83852767]), scaled_genome=None, log_likelihood=1624.655033470184, fitness_values=array([0.68016585, 0.1992444 , 0.1992444 , 0.19143206, 0.11215485,
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+       0.04943766, 0.09557111, 0.01340939, 0.10094998, 0.25494025,
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+       0.13970699, 0.09697349, 0.05269119, 0.05269119, 0.04038897]))]"
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+       0.02558973, 0.02554535, 0.02552444, 0.02549688, 0.0254899 ]))]"
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+       0.04213673, 0.03520517, 0.03247948, 0.02227565, 0.34738108,
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+       0.04209714, 0.03518647, 0.03249756, 0.02222741, 0.34739897,
+       0.50108264, 0.78011036]), scaled_genome=None, log_likelihood=2235.4904122379703, fitness_values=array([0.34787166, 0.10246612, 0.0481583 , 0.03128487, 0.02771965,
+       0.02736542, 0.02732756, 0.0273186 , 0.02731377, 0.02731307])), Solution(fitness=0.027312859950315883, genome=array([0.16771534, 0.23194561, 0.2706846 , 0.34204275, 0.04421941,
+       0.04217486, 0.03517365, 0.03249613, 0.02225402, 0.34739672,
+       0.50113899, 0.78011696]), scaled_genome=None, log_likelihood=2234.8663647465723, fitness_values=array([0.21375602, 0.10812469, 0.05766088, 0.03169333, 0.02769879,
+       0.0273663 , 0.02732701, 0.02732073, 0.02731388, 0.02731286]))]"
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+       0.0420201 , 0.0400663 , 0.03900205, 0.038548  , 0.03834883]))]"
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+       0.06940687, 0.0253856 , 0.05600068, 0.05088184, 0.26655816,
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+       0.0847004 , 0.05761535, 0.04128394, 0.03918266, 0.03143251]))]"
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+       0.02280118, 0.01531903, 0.01406184, 0.01403238, 0.01401845]))]"
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+       0.07061065, 0.05407054, 0.018832  , 0.01416537, 0.01410752]))]"
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+       0.05501648, 0.06448871, 0.11110312, 0.15222698, 0.33724027,
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+       0.09281832, 0.07641782, 0.04748831, 0.04748831, 0.03493615]))]"
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+       0.26857587, 0.26747968, 0.26737061, 0.26736202, 0.26736101]))]"
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+       0.20032952, 0.18372941, 0.15864016, 0.13488765, 0.12188746]))]"
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+       0.03664635, 0.03293115, 0.02630733, 0.02615889, 0.02613412]))]"
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+       0.1944831 , 0.1944831 , 0.1944831 , 0.19377872, 0.19377872]))]"
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+       0.2993088 , 0.28501366, 0.26993036, 0.25396199, 0.242447  ]))]"
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+       0.04516152, 0.01344798, 0.11579645, 0.08285435, 0.42908032,
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+       0.27073233, 0.20849098, 0.17026368, 0.15740159, 0.07963095]))]"
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+       0.68718877, 0.95392143]), scaled_genome=None, log_likelihood=1368.8017061088904, fitness_values=array([0.23547298, 0.07162876, 0.046039  , 0.03291573, 0.02724985,
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+       0.09835554, 0.05693547, 0.06121451, 0.2497862 , 0.34758245,
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+       0.09864419, 0.05709604, 0.06145869, 0.25029855, 0.35088251,
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+       0.02348178, 0.02286871, 0.02265672, 0.02251832, 0.02245836]))]"
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+       0.00531704, 0.01640333, 0.08126384, 0.20161825, 0.56607469,
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+       0.00531788, 0.01636594, 0.08140201, 0.20160724, 0.56607391,
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+       0.02300079, 0.02282894, 0.02280253, 0.02277437, 0.0227688 ])), Solution(fitness=0.022788817820748278, genome=array([0.55548996, 0.10605434, 0.18490298, 0.16429596, 0.10248901,
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+       0.0053187 , 0.01638581, 0.08128882, 0.20152221, 0.5660742 ,
+       0.78270198, 0.9938731 ]), scaled_genome=None, log_likelihood=1560.2407188091345, fitness_values=array([0.62399311, 0.30030943, 0.12655759, 0.03898504, 0.02537946,
+       0.02302674, 0.02282351, 0.02279077, 0.02277355, 0.02276872])), Solution(fitness=0.022768047312390318, genome=array([0.55544757, 0.10609358, 0.18452255, 0.16185048, 0.10247414,
+       0.00531624, 0.01639579, 0.0812863 , 0.201639  , 0.56607352,
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+       0.0229833 , 0.02282496, 0.02278404, 0.02277081, 0.02276805]))]"
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+       0.00599155, 0.01776473, 0.12725579, 0.18551125, 0.564201  ,
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+       0.19453435, 0.15188011, 0.12597551, 0.06163322, 0.04313934]))]"
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+       0.0658773 , 0.1255066 , 0.06644107, 0.12929916, 0.26356177,
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+       0.09192077, 0.07104224, 0.05532903, 0.04653591, 0.03456909]))]"
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+       0.02487086, 0.02472491, 0.02468311, 0.02466745, 0.02466362]))]"
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+       0.06856774, 0.02922622, 0.02281056, 0.02262792, 0.02260333]))]"
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+       0.05988029, 0.04976175, 0.00840102, 0.183282  , 0.33419461,
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+       0.07480086, 0.07480086, 0.06457051, 0.0571004 , 0.05162703]))]"
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+       0.04794794, 0.10174495, 0.06945099, 0.28180588, 0.64027395,
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+       0.13746994, 0.12834758, 0.08943144, 0.08943144, 0.08032315]))]"
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+       0.12868363, 0.12823317, 0.12819691, 0.12817559, 0.12817333]))]"
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+       0.8880831 , 1.01007712]), scaled_genome=None, log_likelihood=1110.3229806447002, fitness_values=array([0.34235337, 0.18396795, 0.11536769, 0.11304103, 0.10600504,
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+       0.10468732, 0.0643066 , 0.05010399, 0.03836961, 0.03285098]))]"
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+       0.03145535, 0.03127673, 0.03123955, 0.03121997, 0.03121804]))]"
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+       0.02475792, 0.02471487, 0.02470443, 0.02469777, 0.0246901 ]))]"
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+       8.49410566e-01]), scaled_genome=None, log_likelihood=1813.4888401575272, fitness_values=array([0.07131294, 0.04484067, 0.03572496, 0.03484983, 0.03450976,
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+       0.04552127, 0.29097587, 0.64329002, 0.94959493]), scaled_genome=None, log_likelihood=4327.313657968971, fitness_values=array([0.51505303, 0.18280673, 0.06863135, 0.04811601, 0.0328458 ,
+       0.02897953, 0.0265969 , 0.02584702, 0.02568068, 0.02563698])), Solution(fitness=0.025610482649407938, genome=array([0.0550184 , 0.69144631, 0.27125567, 0.06420314, 0.01996751,
+       0.04549223, 0.28978276, 0.64333307, 0.94972934]), scaled_genome=None, log_likelihood=4325.800271567797, fitness_values=array([0.43003953, 0.16735875, 0.08781382, 0.04382816, 0.03007972,
+       0.02853385, 0.02678919, 0.02590828, 0.02570459, 0.02561048]))]"
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+       0.04558687, 0.290421  , 0.64331325, 0.94974008]), scaled_genome=None, log_likelihood=4321.878459685377, fitness_values=array([0.16785652, 0.06076286, 0.0335733 , 0.02765342, 0.02583472,
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+       0.0255278 , 0.02551488, 0.02547954, 0.02545856, 0.02545856]))]"
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+       1.30281669e-01, 1.08149225e-01, 4.73071366e-04, 1.37872616e-01,
+       8.16862780e-01]), scaled_genome=None, log_likelihood=1723.128448386655, fitness_values=array([0.2134928 , 0.07953927, 0.04176612, 0.03342826, 0.02720088,
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+       8.15404926e-01]), scaled_genome=None, log_likelihood=1684.6653910503767, fitness_values=array([0.17543042, 0.04803473, 0.03249896, 0.02814626, 0.02733454,
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+        8.18179573e-01]), scaled_genome=None, log_likelihood=1695.4371865473613, fitness_values=array([0.17572696, 0.06084837, 0.03847463, 0.03519153, 0.02791568,
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+        8.15904379e-01]), scaled_genome=None, log_likelihood=1695.0080158517412, fitness_values=array([0.12756329, 0.04372409, 0.03893298, 0.0312324 , 0.02705548,
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+       0.10888497, 0.00160446, 0.14552969, 0.81803968]), scaled_genome=None, log_likelihood=1692.1462108739915, fitness_values=array([0.16712735, 0.14287076, 0.0797817 , 0.04205374, 0.03043678,
+       0.02625053, 0.02516735, 0.02418908, 0.0240209 , 0.02378564]))]"
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+       0.0238041 , 0.02360645, 0.02358648, 0.02353617, 0.02353334])), Solution(fitness=0.13560728146684042, genome=array([1.        , 0.03      , 0.08419409, 0.10435188, 0.13767139,
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+       1.31171912e-01, 1.06790667e-01, 5.29262327e-04, 1.37661630e-01,
+       8.15385830e-01]), scaled_genome=None, log_likelihood=1697.9838452455647, fitness_values=array([0.19483645, 0.06182126, 0.03715825, 0.02584126, 0.02420746,
+       0.02376262, 0.02359097, 0.0235445 , 0.02353375, 0.02353325])), Solution(fitness=0.02357560196502966, genome=array([5.72312308e-01, 3.77518045e-01, 6.38333209e-02, 6.82427307e-02,
+       1.31316246e-01, 1.07818295e-01, 7.62976581e-04, 1.39141245e-01,
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+       0.0239575 , 0.02365937, 0.02358626, 0.023577  , 0.0235756 ]))]"
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+       0.02331661, 0.02156094, 0.02092437, 0.02054034, 0.02045462]))]"
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+       0.03814112, 0.03679122, 0.03590541, 0.03412731, 0.03380437]))]"
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+       0.09163883, 0.00413219, 0.516832  , 0.97000629]), scaled_genome=None, log_likelihood=-250.75724990064919, fitness_values=array([0.07936189, 0.03087887, 0.02414172, 0.02392658, 0.02365895,
+       0.02360148, 0.02357044, 0.02355168, 0.02355168, 0.02353794]))]"
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+       0.0264942 , 0.02641026, 0.0263888 , 0.02638353, 0.02638264]))]"
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+       0.2362174 , 0.2361579 , 0.23615226, 0.23615091, 0.23615075]))]"
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+       0.03162208, 0.03122173, 0.03116955, 0.03115295, 0.03113924]))]"
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+       0.06038694, 0.29701857, 0.4982667 , 0.81923475]), scaled_genome=None, log_likelihood=5363.899015808574, fitness_values=array([0.89248386, 0.23565069, 0.16709313, 0.07248656, 0.03716111,
+       0.03410513, 0.03397616, 0.03396299, 0.03395786, 0.03395777]))]"
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+       0.02342484, 0.0234151 , 0.02340922, 0.02340823, 0.02340814]))]"
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+       0.02155263, 0.021541  , 0.02153107, 0.02152803, 0.02152767])), Solution(fitness=0.02148816210721627, genome=array([0.17226644, 0.43102696, 0.40921265, 0.0571122 , 0.02760027,
+       0.07314743, 0.05117763, 0.67917089, 0.84215293]), scaled_genome=None, log_likelihood=2248.798392268586, fitness_values=array([0.27614343, 0.06599096, 0.02746958, 0.02218023, 0.02164724,
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+       0.0731143 , 0.05120057, 0.67917994, 0.84226987]), scaled_genome=None, log_likelihood=2247.8771456891413, fitness_values=array([0.15294686, 0.04444932, 0.02459282, 0.02177277, 0.02158778,
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+       0.07308005, 0.05114685, 0.67918709, 0.84238919]), scaled_genome=None, log_likelihood=2245.8970115118873, fitness_values=array([0.30927142, 0.14022862, 0.04420585, 0.02274935, 0.02163121,
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+       0.02156694, 0.02153968, 0.02152284, 0.02151098, 0.02151051])), Solution(fitness=0.02148655953110656, genome=array([0.17218164, 0.43154859, 0.40757673, 0.05710201, 0.02762807,
+       0.07306792, 0.05114552, 0.67918818, 0.84243185]), scaled_genome=None, log_likelihood=2245.2369562426466, fitness_values=array([0.16097644, 0.06371054, 0.02528868, 0.02182916, 0.0216139 ,
+       0.02154796, 0.02152201, 0.02149701, 0.02148738, 0.02148656])), Solution(fitness=0.021490109625474395, genome=array([0.17211667, 0.43478412, 0.40876556, 0.05708625, 0.02749222,
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+       0.02154241, 0.02151139, 0.02149601, 0.02149053, 0.02149011]))]"
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+       0.0427248 , 0.02856418, 0.0237194 , 0.0223205 , 0.0221077 ]))]"
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+       0.02779208, 0.02778046, 0.02777769, 0.02777689, 0.02777678])), Solution(fitness=0.027776766339479418, genome=array([0.42119713, 0.41468248, 0.1793538 , 0.02561583, 0.09047307,
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+       0.03218955, 0.16976436, 0.3279515 , 0.85951851]), scaled_genome=None, log_likelihood=2482.4550099053686, fitness_values=array([0.17741562, 0.05526849, 0.02959138, 0.0279473 , 0.02782296,
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+       0.02807866, 0.02782558, 0.02779026, 0.02777986, 0.0277773 ])), Solution(fitness=0.027778218783592407, genome=array([0.42083494, 0.41510513, 0.1793611 , 0.02559636, 0.09047081,
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+       0.02795182, 0.02782559, 0.02780748, 0.02780401, 0.0278038 ]))]"
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+       0.02820873, 0.02794687, 0.02788567, 0.02786522, 0.02781832]))]"
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+       0.02780697, 0.02779398, 0.02778527, 0.02778247, 0.0277775 ]))]"
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+       0.09158837, 0.1760706 , 0.4696029 , 0.73560866]), scaled_genome=None, log_likelihood=1694.2635816118075, fitness_values=array([0.13438857, 0.03577445, 0.02401953, 0.02327691, 0.02318119,
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+       0.0915934 , 0.17607409, 0.46960234, 0.73561284]), scaled_genome=None, log_likelihood=1694.2346879775191, fitness_values=array([0.14215023, 0.03866488, 0.02411982, 0.02324161, 0.02319514,
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+       0.02316816, 0.02315679, 0.02315375, 0.02315318, 0.02315315]))]"
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+       0.09158984, 0.17603298, 0.46958704, 0.7355733 ]), scaled_genome=None, log_likelihood=1697.1041565634803, fitness_values=array([0.08335182, 0.03589604, 0.02433384, 0.02337709, 0.02324984,
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+       0.09145936, 0.17611145, 0.46960309, 0.73525215]), scaled_genome=None, log_likelihood=1690.1818733130012, fitness_values=array([0.17790994, 0.04321721, 0.0246249 , 0.02349555, 0.02323693,
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+       0.64355297, 0.63732901, 0.63057453, 0.62726539, 0.6259218 ]))]"
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+        0.10341783, -0.03708252,  0.09378885,  0.69042182]), scaled_genome=None, log_likelihood=868.1459891281795, fitness_values=array([0.27185419, 0.21978595, 0.20072414, 0.19943647, 0.1992659 ,
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+        0.10341667, -0.03709733,  0.09372991,  0.69042676]), scaled_genome=None, log_likelihood=868.3428166587123, fitness_values=array([0.2731331 , 0.21338926, 0.2008584 , 0.1994229 , 0.19927038,
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+        0.10341621, -0.03710975,  0.09369208,  0.69042678]), scaled_genome=None, log_likelihood=868.274964848071, fitness_values=array([0.30651876, 0.22062548, 0.20137549, 0.19960451, 0.19927876,
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+       0.11307133, 0.02901111, 0.636751  , 0.73453847]), scaled_genome=None, log_likelihood=900.535327136703, fitness_values=array([0.24123091, 0.11504276, 0.07424749, 0.03922049, 0.02784481,
+       0.02726993, 0.02720615, 0.02718823, 0.02718562, 0.0271852 ]))]"
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+       0.06474303, 0.06449939, 0.06383233, 0.06379503, 0.06371088]))]"
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+       0.02365205, 0.02346071, 0.02344672, 0.02343633, 0.023436  ])), Solution(fitness=0.023433313759491457, genome=array([0.10950566, 0.84854506, 0.05340429, 0.01285383, 0.03901591,
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+       0.0237147 , 0.02350219, 0.02345234, 0.02343985, 0.02343401]))]"
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+       9.58009212e-01]), scaled_genome=None, log_likelihood=1593.4847013562385, fitness_values=array([0.14357057, 0.08030896, 0.02455422, 0.02309773, 0.02285969,
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+       0.02277676, 0.02274432, 0.02271853, 0.02270904, 0.0227063 ]))]"
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+       0.02291854, 0.02279754, 0.0227907 , 0.02277564, 0.02276756])), Solution(fitness=0.02275387934148157, genome=array([1.82561213e-01, 3.54065549e-01, 4.75000449e-01, 6.26392008e-02,
+       6.79455508e-02, 3.84930374e-02, 8.15421271e-04, 3.11074038e-01,
+       9.58004778e-01]), scaled_genome=None, log_likelihood=1596.2813533796361, fitness_values=array([0.13129047, 0.09468673, 0.03921689, 0.02405352, 0.02302631,
+       0.0229007 , 0.02280473, 0.02278018, 0.02278018, 0.02275388]))]"
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+ 0.026778   0.02435319 0.02414021 0.023854  ]",0.0,"[Solution(fitness=0.027935230383882032, genome=array([1.76388608e-01, 3.54339006e-01, 4.69272386e-01, 6.29167825e-02,
+       6.79233835e-02, 3.90438415e-02, 5.31895691e-04, 3.10980445e-01,
+       9.54069002e-01]), scaled_genome=None, log_likelihood=1554.6838366267098, fitness_values=array([0.11455514, 0.0685187 , 0.05410506, 0.03836454, 0.03464419,
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+       0.02752153, 0.02649615, 0.02635059, 0.02635059, 0.02591116])), Solution(fitness=0.025951391566317903, genome=array([0.18390505, 0.34885817, 0.46723679, 0.06344623, 0.07049023,
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+        9.56272373e-01]), scaled_genome=None, log_likelihood=1560.3439043342687, fitness_values=array([0.17177109, 0.08003299, 0.04105642, 0.03693873, 0.03265508,
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+       6.92179956e-02, 3.91409104e-02, 2.85044220e-04, 3.12938179e-01,
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+       0.02388693, 0.02388693, 0.02385732, 0.023854  , 0.023854  ]))]"
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+       9.57879008e-01]), scaled_genome=None, log_likelihood=1596.8532178008386, fitness_values=array([0.27462655, 0.15209724, 0.10390401, 0.03824137, 0.02468188,
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+       9.57897860e-01]), scaled_genome=None, log_likelihood=1599.8193637182349, fitness_values=array([0.30104787, 0.13961304, 0.09399305, 0.03257798, 0.02410259,
+       0.02307001, 0.02285451, 0.022756  , 0.02273782, 0.02273495])), Solution(fitness=0.022710060992944738, genome=array([1.83713872e-01, 3.51941005e-01, 4.74094481e-01, 6.31138945e-02,
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+       9.57999389e-01]), scaled_genome=None, log_likelihood=1590.8949783943315, fitness_values=array([0.32777379, 0.12286266, 0.07665284, 0.02981275, 0.02434525,
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+       0.02295483, 0.02278423, 0.02273957, 0.02271935, 0.02271136])), Solution(fitness=0.02270971607152068, genome=array([0.18374211, 0.35191381, 0.47406618, 0.06314531, 0.06822204,
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+       0.02292144, 0.02279737, 0.02273044, 0.02271673, 0.02271317]))]"
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+       0.048987  , 0.11640812, 0.49824346, 0.78139901]), scaled_genome=None, log_likelihood=1977.4015816545848, fitness_values=array([0.26305375, 0.16951083, 0.0884358 , 0.06125723, 0.0371988 ,
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+       0.04902281, 0.11660576, 0.49860712, 0.78133126]), scaled_genome=None, log_likelihood=1979.2075178001053, fitness_values=array([0.24695196, 0.21962553, 0.10865413, 0.05365793, 0.03757516,
+       0.03057187, 0.02837887, 0.027692  , 0.02766793, 0.02758519])), Solution(fitness=0.02761247173027184, genome=array([0.16363411, 0.33705092, 0.51465073, 0.05068354, 0.04156777,
+       0.04889476, 0.11650154, 0.49855037, 0.78143101]), scaled_genome=None, log_likelihood=1973.1249986614725, fitness_values=array([0.19995022, 0.14853827, 0.07702909, 0.03518802, 0.03518802,
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+       0.04910742, 0.11665354, 0.49861669, 0.78142612]), scaled_genome=None, log_likelihood=1978.4936154169886, fitness_values=array([0.28213604, 0.13358166, 0.09082867, 0.05357414, 0.03354148,
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+       0.04914454, 0.11638027, 0.49843236, 0.78156251]), scaled_genome=None, log_likelihood=1976.8718586844107, fitness_values=array([0.24791243, 0.13656655, 0.06774673, 0.04090826, 0.0332368 ,
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+       0.04917145, 0.11616545, 0.49848996, 0.78137811]), scaled_genome=None, log_likelihood=1970.4266624390727, fitness_values=array([0.18274956, 0.04250035, 0.03051255, 0.02801518, 0.02763201,
+       0.02755102, 0.02751627, 0.02751195, 0.02750348, 0.02750034])), Solution(fitness=0.027501397499756133, genome=array([0.16209448, 0.33646555, 0.51599483, 0.05100271, 0.04157545,
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+       0.02759313, 0.02752567, 0.02751272, 0.02750302, 0.0275014 ])), Solution(fitness=0.02749522132926146, genome=array([0.16213005, 0.33581683, 0.51673525, 0.05098445, 0.0414675 ,
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+       0.02757423, 0.0275342 , 0.02751036, 0.027509  , 0.02750361]))]"
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+       0.40489822, 0.40121304, 0.39786316, 0.39589076, 0.38892054]))]"
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+       0.02762424, 0.02757989, 0.0275401 , 0.0275401 , 0.02752361]))]"
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+       0.11381931, 0.13513852, 0.52145618, 0.97856385]), scaled_genome=None, log_likelihood=4128.072114191187, fitness_values=array([0.88724399, 0.23368119, 0.10779293, 0.05610381, 0.03294848,
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+       0.1144024 , 0.13723688, 0.52136307, 0.97819519]), scaled_genome=None, log_likelihood=4120.490463065391, fitness_values=array([0.72420452, 0.11975869, 0.07065267, 0.03975661, 0.03266769,
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+       0.11310073, 0.13840838, 0.52143257, 0.97716542]), scaled_genome=None, log_likelihood=4125.033423417159, fitness_values=array([0.21703172, 0.1194874 , 0.06637889, 0.04791819, 0.0290729 ,
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+       0.11313894, 0.13583516, 0.52142705, 0.97621753]), scaled_genome=None, log_likelihood=4122.498437138565, fitness_values=array([0.68622064, 0.29865419, 0.10594768, 0.04948026, 0.03682612,
+       0.0311596 , 0.02813033, 0.02769044, 0.02730124, 0.02716507]))]"
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+       0.03962981, 0.15109123, 0.26397174, 0.97323746]), scaled_genome=None, log_likelihood=4008.9301467786795, fitness_values=array([1.00303423, 0.54771673, 0.17093868, 0.06203235, 0.03557647,
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+       0.03965441, 0.15108984, 0.26364244, 0.97330685]), scaled_genome=None, log_likelihood=4008.7156351868, fitness_values=array([0.74396676, 0.33878835, 0.15170282, 0.04010637, 0.03262341,
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+       0.03087972, 0.03071   , 0.03071   , 0.03066591, 0.0306659 ])), Solution(fitness=0.03066812567494374, genome=array([0.19732782, 0.10296778, 0.7163986 , 0.00600403, 0.10074703,
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+       0.03080712, 0.03070879, 0.0306835 , 0.03065756, 0.03065756]))]"
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+       0.08980501, 0.03700897, 0.3642639 , 0.83108262]), scaled_genome=None, log_likelihood=2126.2722310478707, fitness_values=array([0.15945473, 0.11007947, 0.047748  , 0.0330941 , 0.02899179,
+       0.02720777, 0.02686371, 0.02662659, 0.02652522, 0.02650705])), Solution(fitness=0.026483802984507983, genome=array([0.13774119, 0.72111106, 0.14893306, 0.09257909, 0.04474277,
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+       0.09084439, 0.03688417, 0.36398041, 0.83117122]), scaled_genome=None, log_likelihood=2141.051086563143, fitness_values=array([0.31195973, 0.13180241, 0.06779572, 0.04905592, 0.0366938 ,
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+       0.09068438, 0.03648351, 0.36386676, 0.8322739 ]), scaled_genome=None, log_likelihood=2131.2118503646775, fitness_values=array([0.31243984, 0.13027933, 0.05271945, 0.03976033, 0.03437984,
+       0.03015424, 0.02747511, 0.02692783, 0.02665446, 0.02658698]))]"
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+       0.07340931, 0.17055685, 0.45507026, 0.83661363]), scaled_genome=None, log_likelihood=1552.7698451763952, fitness_values=array([0.0581972 , 0.02988212, 0.02764254, 0.02741494, 0.02739238,
+       0.02738301, 0.02738137, 0.02738095, 0.02738088, 0.02738086])), Solution(fitness=0.02738083846869396, genome=array([0.03985294, 0.7679047 , 0.20839679, 0.08900168, 0.08032239,
+       0.0734096 , 0.17053128, 0.45506936, 0.83661341]), scaled_genome=None, log_likelihood=1553.014611912793, fitness_values=array([0.08319743, 0.03029103, 0.02778756, 0.02745661, 0.02739364,
+       0.02738275, 0.02738139, 0.02738092, 0.02738085, 0.02738084]))]"
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+       0.07298135, 0.17108305, 0.4550042 , 0.83696418]), scaled_genome=None, log_likelihood=1556.5850368283518, fitness_values=array([0.08662867, 0.08662867, 0.04051139, 0.03244462, 0.03000916,
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+       0.02779272, 0.02767262, 0.02752344, 0.02741151, 0.02741151])), Solution(fitness=0.027412309242480688, genome=array([0.04100939, 0.7655209 , 0.20737723, 0.09532857, 0.08008305,
+       0.0730311 , 0.1740584 , 0.45504526, 0.83666593]), scaled_genome=None, log_likelihood=1545.846210818217, fitness_values=array([0.19138411, 0.08671249, 0.04672321, 0.02985528, 0.0284703 ,
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+       0.07359436, 0.17512274, 0.45516216, 0.83635267]), scaled_genome=None, log_likelihood=1547.0814451955503, fitness_values=array([0.11030984, 0.04747337, 0.03299843, 0.03048292, 0.02797042,
+       0.02770669, 0.02757254, 0.02749374, 0.02747506, 0.02742522])), Solution(fitness=0.02743677508841693, genome=array([0.03917558, 0.7695849 , 0.20696288, 0.08703235, 0.08046871,
+       0.07310828, 0.16673236, 0.45475008, 0.83647475]), scaled_genome=None, log_likelihood=1551.8767077355626, fitness_values=array([0.10065473, 0.07422672, 0.04144826, 0.03457776, 0.02952318,
+       0.0281848 , 0.0276838 , 0.02752352, 0.02747252, 0.02743678])), Solution(fitness=0.0274466998533975, genome=array([0.03983601, 0.76912831, 0.20819833, 0.08637221, 0.08034573,
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+       0.0278027 , 0.02746011, 0.02745816, 0.0274098 , 0.02740664])), Solution(fitness=0.02742546347876458, genome=array([0.03910567, 0.76705332, 0.20846119, 0.0875918 , 0.08020773,
+       0.0737488 , 0.16731393, 0.45462149, 0.83603253]), scaled_genome=None, log_likelihood=1548.6132175888738, fitness_values=array([0.12018343, 0.05774553, 0.04594491, 0.03141472, 0.02856912,
+       0.02801686, 0.02754695, 0.02746543, 0.02743149, 0.02742546]))]"
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+       0.07337288, 0.17215266, 0.4550243 , 0.83668804]), scaled_genome=None, log_likelihood=1548.5399739116197, fitness_values=array([0.0791453 , 0.03978601, 0.02873781, 0.02773438, 0.02745098,
+       0.02740962, 0.02740427, 0.027398  , 0.02739039, 0.0273799 ])), Solution(fitness=0.02738873636959151, genome=array([0.04066517, 0.76736037, 0.20823061, 0.09109253, 0.08027722,
+       0.07337831, 0.17293285, 0.45504668, 0.8366072 ]), scaled_genome=None, log_likelihood=1553.1866251605597, fitness_values=array([0.13098951, 0.03554125, 0.03070295, 0.02777197, 0.02754153,
+       0.02745043, 0.02740737, 0.02740157, 0.0273896 , 0.02738874])), Solution(fitness=0.027381478669431105, genome=array([0.04081545, 0.76581582, 0.20799104, 0.09481893, 0.08024416,
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+       0.07334279, 0.17005521, 0.45492818, 0.83664927]), scaled_genome=None, log_likelihood=1552.7627267040323, fitness_values=array([0.12416509, 0.03903232, 0.03069144, 0.02789424, 0.02751775,
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+       0.07339681, 0.17492407, 0.45525213, 0.83643261]), scaled_genome=None, log_likelihood=1548.1656969505702, fitness_values=array([0.09659532, 0.03507533, 0.02959364, 0.02771316, 0.02748731,
+       0.02743137, 0.02740858, 0.02739801, 0.02738976, 0.02737886]))]"
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+       0.07290986, 0.25160539, 0.46469831, 0.82932759]), scaled_genome=None, log_likelihood=1488.1525939010878, fitness_values=array([0.10203835, 0.06914445, 0.06132026, 0.05398108, 0.05173022,
+       0.05173022, 0.0447841 , 0.04198792, 0.04027875, 0.03912798])), Solution(fitness=0.028820469416705095, genome=array([0.05206662, 0.74359781, 0.20433557, 0.10269466, 0.07891044,
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+       0.05437919, 0.0455875 , 0.04543185, 0.03770726, 0.03474425])), Solution(fitness=0.027867538094015133, genome=array([0.03746661, 0.75512287, 0.20741052, 0.08397507, 0.08068177,
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+       0.05073252, 0.05029579, 0.04899335, 0.04770927, 0.04765591])), Solution(fitness=0.0370208727592931, genome=array([0.08117077, 0.72209491, 0.19673432, 0.10397695, 0.07544037,
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+       0.04205328, 0.04171202, 0.03904704, 0.0372089 , 0.03702087]))]"
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+       0.07340782, 0.17071124, 0.45507565, 0.83661473]), scaled_genome=None, log_likelihood=1551.9716978569836, fitness_values=array([0.16231584, 0.10465402, 0.04456307, 0.03168789, 0.0283092 ,
+       0.02746234, 0.02740396, 0.02738417, 0.02738111, 0.027381  ])), Solution(fitness=0.027380900865555752, genome=array([0.03979614, 0.76773737, 0.20839688, 0.08894176, 0.08033286,
+       0.07340969, 0.17039608, 0.4550589 , 0.83661336]), scaled_genome=None, log_likelihood=1552.3568314811973, fitness_values=array([0.20494951, 0.05955353, 0.03498808, 0.02894077, 0.02755943,
+       0.02742457, 0.02739153, 0.02738168, 0.02738099, 0.0273809 ])), Solution(fitness=0.027381067387404624, genome=array([0.03982189, 0.76759869, 0.20838282, 0.08895425, 0.0803243 ,
+       0.07340167, 0.17045064, 0.45508422, 0.83661859]), scaled_genome=None, log_likelihood=1551.9791903581481, fitness_values=array([0.10251188, 0.05982932, 0.03299168, 0.03015567, 0.02763634,
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+       0.07341102, 0.17088863, 0.455052  , 0.83661119]), scaled_genome=None, log_likelihood=1552.3684472665875, fitness_values=array([0.13175359, 0.06132885, 0.04969994, 0.03128119, 0.02805404,
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+       0.02742937, 0.02738666, 0.02738341, 0.02738144, 0.02738099])), Solution(fitness=0.027381454083950884, genome=array([0.04015012, 0.76781473, 0.20837459, 0.08929613, 0.08029483,
+       0.073396  , 0.17122896, 0.4551102 , 0.83662321]), scaled_genome=None, log_likelihood=1553.5411082201142, fitness_values=array([0.22145707, 0.06972543, 0.03863218, 0.02973006, 0.02789312,
+       0.02745012, 0.02740118, 0.0273844 , 0.02738207, 0.02738145]))]"
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+       0.02775015, 0.02755719, 0.02746214, 0.02744013, 0.02741392]))]"
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+       0.0073272 , 0.30348021, 0.44751955, 0.52522736]), scaled_genome=None, log_likelihood=7334.140176456349, fitness_values=array([1.27941229, 0.44134533, 0.09345275, 0.03693138, 0.02948527,
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+       0.0073257 , 0.30357356, 0.44752975, 0.52522488]), scaled_genome=None, log_likelihood=7334.714367351648, fitness_values=array([0.94365283, 0.29576307, 0.10922969, 0.03835602, 0.02930604,
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+       0.13141239, 0.03295001, 0.02919172, 0.02802594, 0.02795127]))]"
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+       0.0347843 , 0.03224796, 0.03091463, 0.0305055 , 0.03012414]))]"
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+       0.0280414 , 0.02759135, 0.02718698, 0.02711001, 0.02706512]))]"
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+       0.7946011 ]), scaled_genome=None, log_likelihood=2629.7447233911535, fitness_values=array([0.02701439, 0.02443779, 0.0242594 , 0.02421894, 0.02421186,
+       0.02420918, 0.02420863, 0.02420859, 0.02420859, 0.02420859])), Solution(fitness=0.024265877085467066, genome=array([0.63720578, 0.37665539, 0.03983345, 0.02603383, 0.54374677,
+       0.79458474]), scaled_genome=None, log_likelihood=2623.253677913295, fitness_values=array([0.02946059, 0.0243755 , 0.02429798, 0.02426813, 0.02426593,
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+       0.79460108]), scaled_genome=None, log_likelihood=2629.744341485575, fitness_values=array([0.03298467, 0.02446183, 0.02425959, 0.02424028, 0.02422244,
+       0.0242105 , 0.02420875, 0.0242086 , 0.02420859, 0.02420859]))]"
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+       0.02424565, 0.02423755, 0.02423014, 0.02422175, 0.02421387])), Solution(fitness=0.024216937630905606, genome=array([0.64056193, 0.37704657, 0.04005479, 0.02601913, 0.54357842,
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+       0.02426039, 0.02424739, 0.02423935, 0.02422683, 0.02421694])), Solution(fitness=0.02421447789279177, genome=array([0.64060381, 0.37703516, 0.04005589, 0.02600839, 0.54357358,
+       0.79462344]), scaled_genome=None, log_likelihood=2630.585447303978, fitness_values=array([0.0584254 , 0.02649057, 0.02458366, 0.0243354 , 0.02430865,
+       0.0242603 , 0.02425363, 0.02423251, 0.02422411, 0.02421448])), Solution(fitness=0.024212997050372477, genome=array([0.64050945, 0.37702991, 0.04005577, 0.0260102 , 0.54357783,
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+       0.02423513, 0.02422308, 0.02421891, 0.02421363, 0.024213  ])), Solution(fitness=0.02421989979661441, genome=array([0.64073873, 0.37713774, 0.04005716, 0.02600395, 0.5435724 ,
+       0.79463285]), scaled_genome=None, log_likelihood=2631.0509653657327, fitness_values=array([0.03535758, 0.02616448, 0.02452613, 0.02435149, 0.02428361,
+       0.02426284, 0.02423083, 0.02422976, 0.02422143, 0.0242199 ])), Solution(fitness=0.024218598769798955, genome=array([0.64047677, 0.37677774, 0.04006639, 0.02602659, 0.54358266,
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+       0.02425224, 0.02425224, 0.0242348 , 0.02422546, 0.0242186 ])), Solution(fitness=0.024228823008154723, genome=array([0.6393088 , 0.37691242, 0.04008762, 0.02602442, 0.54356267,
+       0.79460081]), scaled_genome=None, log_likelihood=2627.773269814643, fitness_values=array([0.05944789, 0.02761219, 0.02453841, 0.02436401, 0.02429024,
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+       0.79458463]), scaled_genome=None, log_likelihood=2629.7097452484695, fitness_values=array([0.06277795, 0.0254361 , 0.02450072, 0.02435895, 0.02428139,
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+       0.02424791, 0.02423742, 0.02422106, 0.02421618, 0.0242128 ]))]"
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+       0.02426968, 0.0242673 , 0.0242664 , 0.02426635, 0.02426608])), Solution(fitness=0.02426610383470486, genome=array([0.63732846, 0.37668952, 0.0398352 , 0.02603158, 0.54373873,
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+       0.02427031, 0.0242676 , 0.02426649, 0.02426624, 0.0242661 ])), Solution(fitness=0.024213453299739028, genome=array([0.64049688, 0.37706619, 0.04005887, 0.02600263, 0.54357629,
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+       0.794608  ]), scaled_genome=None, log_likelihood=2630.0477561653415, fitness_values=array([0.04096578, 0.02468258, 0.02429788, 0.02425311, 0.02423952,
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+       0.7946004 ]), scaled_genome=None, log_likelihood=2630.0092331521764, fitness_values=array([0.05180973, 0.02470811, 0.02437265, 0.02425735, 0.02425034,
+       0.0242362 , 0.02422142, 0.0242186 , 0.02421602, 0.02421363])), Solution(fitness=0.024220772184805846, genome=array([0.64034217, 0.37726985, 0.04006882, 0.02599386, 0.54358547,
+       0.79464132]), scaled_genome=None, log_likelihood=2630.521195000023, fitness_values=array([0.04438343, 0.02470645, 0.02431013, 0.02429173, 0.02426385,
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+       0.79461226]), scaled_genome=None, log_likelihood=2630.0182567476786, fitness_values=array([0.04583786, 0.02460472, 0.02428275, 0.02425219, 0.02424017,
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+       0.7945781 ]), scaled_genome=None, log_likelihood=2629.7439739768283, fitness_values=array([0.03145513, 0.02464467, 0.0242913 , 0.02423741, 0.02422597,
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+       0.02469681, 0.02469681, 0.0246921 , 0.0246921 , 0.0246921 ])), Solution(fitness=0.024749045604276237, genome=array([0.63187983, 0.36812017, 0.03953725, 0.02630798, 0.5432584 ,
+       0.79470193]), scaled_genome=None, log_likelihood=2595.7199395594043, fitness_values=array([0.03606928, 0.0260471 , 0.02549957, 0.02497942, 0.02496115,
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+       0.02467595, 0.02467595, 0.02467595, 0.02467595, 0.0246757 ]))]"
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+       0.79458545]), scaled_genome=None, log_likelihood=2623.2669687862635, fitness_values=array([0.05972146, 0.02914395, 0.02458565, 0.02435074, 0.02428521,
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+       0.02426729, 0.02426597, 0.02426588, 0.02426588, 0.02426588]))]"
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+       0.49498869]), scaled_genome=None, log_likelihood=4016.38347880517, fitness_values=array([0.03206868, 0.02727998, 0.02711589, 0.02710466, 0.02710309,
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+       0.49412423]), scaled_genome=None, log_likelihood=4015.8818114815176, fitness_values=array([0.03737117, 0.02726863, 0.02711554, 0.02710447, 0.02710311,
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+       0.02710305, 0.02710305, 0.02710305, 0.02710305, 0.02710305]))]"
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+       0.4951325 ]), scaled_genome=None, log_likelihood=4017.787851014628, fitness_values=array([0.07265461, 0.03711725, 0.02756899, 0.02712508, 0.0271139 ,
+       0.02710775, 0.02710439, 0.02710324, 0.02710319, 0.02710309])), Solution(fitness=0.02710311991294862, genome=array([0.97218729, 0.03919404, 0.02826257, 0.0150701 , 0.13438859,
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+       0.4951309 ]), scaled_genome=None, log_likelihood=4009.6098280918804, fitness_values=array([0.05133238, 0.03418973, 0.02722057, 0.02712838, 0.0271087 ,
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+       0.0271065 , 0.02710446, 0.02710357, 0.02710321, 0.02710312]))]"
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+       0.49560031]), scaled_genome=None, log_likelihood=4013.410607563687, fitness_values=array([0.11867895, 0.04404427, 0.02953377, 0.02722217, 0.02711715,
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+       0.8577134 ]), scaled_genome=None, log_likelihood=2712.3538527853093, fitness_values=array([0.05939319, 0.04039017, 0.03602769, 0.03382177, 0.03365133,
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+       0.02817213, 0.02817108, 0.02817031, 0.02816993, 0.02816991]))]"
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+       0.5539735 ]), scaled_genome=None, log_likelihood=2913.510874635682, fitness_values=array([0.1039065 , 0.03611284, 0.02867319, 0.02830961, 0.02821354,
+       0.02818054, 0.0281805 , 0.02817392, 0.02816363, 0.02816363])), Solution(fitness=0.028149344424330165, genome=array([0.7361855 , 0.2733366 , 0.027719  , 0.04637997, 0.04519148,
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+       0.02818554, 0.0281706 , 0.02816155, 0.028153  , 0.02814934])), Solution(fitness=0.028151390601575267, genome=array([0.73643495, 0.27310545, 0.02776481, 0.04626039, 0.04523319,
+       0.55397322]), scaled_genome=None, log_likelihood=2914.0706509477714, fitness_values=array([0.11739298, 0.0431172 , 0.02907727, 0.02831509, 0.02820394,
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+       0.02815895, 0.02815682, 0.02815023, 0.02814829, 0.0281461 ]))]"
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+       0.02830145, 0.02830145, 0.02827097, 0.02827097, 0.02826015]))]"
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+       0.55392819]), scaled_genome=None, log_likelihood=2914.476868938303, fitness_values=array([0.18632645, 0.05430289, 0.03028004, 0.02829685, 0.02817237,
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+       0.02521552, 0.02520184, 0.02519658, 0.02519643, 0.02519643])), Solution(fitness=0.025239068206326456, genome=array([0.72649348, 0.28835046, 0.06113465, 0.03066328, 0.33773609,
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+       0.8085034 ]), scaled_genome=None, log_likelihood=1979.77028938224, fitness_values=array([0.03142762, 0.02548527, 0.02523474, 0.02522266, 0.02520366,
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+       0.02519993, 0.02519895, 0.02519871, 0.02519869, 0.02519869]))]"
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+       0.80846523]), scaled_genome=None, log_likelihood=1980.8113651907906, fitness_values=array([0.12314772, 0.02802637, 0.02595595, 0.02526838, 0.02525575,
+       0.02525254, 0.02523004, 0.0252281 , 0.02521024, 0.02520556])), Solution(fitness=0.02520601253783996, genome=array([0.73337115, 0.28867632, 0.06167271, 0.03065123, 0.33729471,
+       0.80857175]), scaled_genome=None, log_likelihood=1980.696237859023, fitness_values=array([0.14819057, 0.03265555, 0.02619419, 0.02539853, 0.0253017 ,
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+       0.025225  , 0.02521947, 0.02521424, 0.0252093 , 0.02520651]))]"
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+       0.02522491, 0.0252062 , 0.02519952, 0.02519871, 0.0251987 ]))]"
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+       0.10312964, 0.10271389, 0.09787097, 0.09447362, 0.09284896]))]"
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+       0.8283461 ]), scaled_genome=None, log_likelihood=772.5349761084685, fitness_values=array([0.05146569, 0.03746778, 0.03723534, 0.03721222, 0.03720904,
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+       0.82832008]), scaled_genome=None, log_likelihood=610.7724462879082, fitness_values=array([0.04690037, 0.04316762, 0.04298229, 0.0429639 , 0.04293853,
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+       0.82825352]), scaled_genome=None, log_likelihood=610.7589686066667, fitness_values=array([0.05157222, 0.04334383, 0.04306057, 0.04300498, 0.0429417 ,
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+       0.82912684]), scaled_genome=None, log_likelihood=612.1291182869941, fitness_values=array([0.04974188, 0.04315421, 0.04304853, 0.0429719 , 0.04296224,
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+       0.82827068]), scaled_genome=None, log_likelihood=610.3249077296018, fitness_values=array([0.05474479, 0.04327125, 0.04301216, 0.04296256, 0.04293243,
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+       0.8293488 ]), scaled_genome=None, log_likelihood=611.328509950517, fitness_values=array([0.04607848, 0.04314456, 0.04301776, 0.04297302, 0.04296073,
+       0.04295657, 0.0429557 , 0.04295064, 0.04295001, 0.04295001]))]"
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+       0.83095703]), scaled_genome=None, log_likelihood=569.7319407001348, fitness_values=array([0.06131309, 0.04517624, 0.04466744, 0.04441203, 0.04441203,
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+       0.82983689]), scaled_genome=None, log_likelihood=570.1863993673295, fitness_values=array([0.05357788, 0.04595373, 0.04439875, 0.04427628, 0.04421412,
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+       0.9042764 ]), scaled_genome=None, log_likelihood=1673.7375100710897, fitness_values=array([0.05321109, 0.02354403, 0.02334889, 0.02332091, 0.02329591,
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+       0.02329606, 0.02327923, 0.02327923, 0.02324968, 0.02324113])), Solution(fitness=0.02325479889585753, genome=array([0.76745119, 0.26064576, 0.05778673, 0.07215542, 0.11655816,
+       0.9042036 ]), scaled_genome=None, log_likelihood=1673.897857554327, fitness_values=array([0.08017198, 0.02879539, 0.02378301, 0.02335879, 0.02333526,
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+       0.8283742 ]), scaled_genome=None, log_likelihood=1029.4006647381889, fitness_values=array([0.08031418, 0.05158148, 0.05112452, 0.05110283, 0.05110093,
+       0.05109847, 0.05109766, 0.05109647, 0.05109065, 0.0510853 ])), Solution(fitness=0.051073912041153004, genome=array([0.12603323, 0.90137506, 0.06658235, 0.10746893, 0.00272349,
+       0.82840463]), scaled_genome=None, log_likelihood=1027.6544448008267, fitness_values=array([0.0611129 , 0.0512022 , 0.05111887, 0.05108874, 0.05108217,
+       0.05107542, 0.05107406, 0.05107392, 0.05107391, 0.05107391])), Solution(fitness=0.051105203027299026, genome=array([0.12747564, 0.90140609, 0.06727983, 0.10746069, 0.00193596,
+       0.82839268]), scaled_genome=None, log_likelihood=1030.6574059932398, fitness_values=array([0.06394709, 0.05130585, 0.05112523, 0.05110774, 0.05110547,
+       0.05110521, 0.0511052 , 0.0511052 , 0.0511052 , 0.0511052 ])), Solution(fitness=0.05110520302076617, genome=array([0.12747552, 0.90140711, 0.06727977, 0.10746088, 0.00193602,
+       0.82839307]), scaled_genome=None, log_likelihood=1030.6590893777418, fitness_values=array([0.05551597, 0.05123272, 0.05112172, 0.05110618, 0.05110535,
+       0.05110522, 0.0511052 , 0.0511052 , 0.0511052 , 0.0511052 ])), Solution(fitness=0.051105203283998514, genome=array([0.12747392, 0.90139331, 0.06727901, 0.10746154, 0.0019369 ,
+       0.82839338]), scaled_genome=None, log_likelihood=1030.6291607491721, fitness_values=array([0.05343039, 0.05128168, 0.05112276, 0.05110717, 0.05110538,
+       0.05110523, 0.05110521, 0.0511052 , 0.0511052 , 0.0511052 ])), Solution(fitness=0.05110520302466733, genome=array([0.12747595, 0.90140807, 0.06727998, 0.10746084, 0.00193579,
+       0.82839304]), scaled_genome=None, log_likelihood=1030.6617973063267, fitness_values=array([0.07457952, 0.05126158, 0.05111046, 0.05110683, 0.05110532,
+       0.05110521, 0.0511052 , 0.0511052 , 0.0511052 , 0.0511052 ])), Solution(fitness=0.05110520310494494, genome=array([0.12747447, 0.90140579, 0.06727927, 0.10746144, 0.0019366 ,
+       0.82839392]), scaled_genome=None, log_likelihood=1030.654329543242, fitness_values=array([0.05928528, 0.05123557, 0.0511253 , 0.05110772, 0.05110539,
+       0.05110524, 0.05110521, 0.0511052 , 0.0511052 , 0.0511052 ])), Solution(fitness=0.05110520304118113, genome=array([0.12747539, 0.90140355, 0.06727971, 0.10746068, 0.0019361 ,
+       0.82839252]), scaled_genome=None, log_likelihood=1030.6520027186998, fitness_values=array([0.05959662, 0.05131215, 0.05113764, 0.05110683, 0.05110537,
+       0.05110522, 0.0511052 , 0.0511052 , 0.0511052 , 0.0511052 ])), Solution(fitness=0.05107391263801673, genome=array([0.12603322, 0.90135176, 0.06658235, 0.10746734, 0.0027235 ,
+       0.8284007 ]), scaled_genome=None, log_likelihood=1027.6098027993694, fitness_values=array([0.06446526, 0.05139131, 0.05111248, 0.051101  , 0.05108597,
+       0.05107544, 0.05107405, 0.05107394, 0.05107392, 0.05107391]))]"
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+       0.82838328]), scaled_genome=None, log_likelihood=1030.8896484229444, fitness_values=array([0.11848224, 0.05390831, 0.0515848 , 0.05119357, 0.05113414,
+       0.05112613, 0.0511121 , 0.05110798, 0.05110686, 0.05110598])), Solution(fitness=0.05108105590735163, genome=array([0.12606299, 0.89979785, 0.06663777, 0.10744132, 0.00280603,
+       0.82827316]), scaled_genome=None, log_likelihood=1024.6952571829456, fitness_values=array([0.07864631, 0.05451696, 0.05141581, 0.05119971, 0.05113569,
+       0.0511107 , 0.05109955, 0.05109699, 0.05108106, 0.05108106])), Solution(fitness=0.051076381629100116, genome=array([0.12601029, 0.90067213, 0.06659711, 0.10747405, 0.0027361 ,
+       0.82838831]), scaled_genome=None, log_likelihood=1026.254108039096, fitness_values=array([0.08985543, 0.0531778 , 0.05162898, 0.05116541, 0.05112998,
+       0.05109594, 0.05108827, 0.0510833 , 0.05107927, 0.05107638])), Solution(fitness=0.05110554351930958, genome=array([0.12740277, 0.90154089, 0.06724759, 0.10747635, 0.00197618,
+       0.82842777]), scaled_genome=None, log_likelihood=1030.7689718243626, fitness_values=array([0.10470005, 0.05402479, 0.0516504 , 0.0511825 , 0.05112071,
+       0.05111351, 0.05110808, 0.05110695, 0.05110587, 0.05110554])), Solution(fitness=0.051077311467237034, genome=array([0.12601673, 0.90172081, 0.06659315, 0.10749807, 0.00273775,
+       0.82849836]), scaled_genome=None, log_likelihood=1028.2824460358047, fitness_values=array([0.1241512 , 0.05392541, 0.05142925, 0.05123696, 0.051152  ,
+       0.0511121 , 0.05109058, 0.05109058, 0.05108246, 0.05107731])), Solution(fitness=0.05108364819821631, genome=array([0.12597484, 0.90028395, 0.0666663 , 0.10737905, 0.00280545,
+       0.82821993]), scaled_genome=None, log_likelihood=1025.468095530982, fitness_values=array([0.09226047, 0.05554255, 0.05158967, 0.05118294, 0.05114546,
+       0.05110259, 0.05110259, 0.05110231, 0.0510918 , 0.05108365])), Solution(fitness=0.051105576900938456, genome=array([0.12747656, 0.90136716, 0.06728548, 0.10743328, 0.00193572,
+       0.82834647]), scaled_genome=None, log_likelihood=1030.5895681134048, fitness_values=array([0.08168615, 0.05287799, 0.05144218, 0.05116345, 0.05113047,
+       0.05111893, 0.05111025, 0.05110657, 0.05110584, 0.05110558])), Solution(fitness=0.05107552685823294, genome=array([0.12602901, 0.90064794, 0.06658045, 0.10749071, 0.00272653,
+       0.82840941]), scaled_genome=None, log_likelihood=1026.238068593666, fitness_values=array([0.07147078, 0.05524384, 0.05150774, 0.0511586 , 0.05113042,
+       0.05109239, 0.05108853, 0.05108149, 0.05107835, 0.05107553])), Solution(fitness=0.051082133048279146, genome=array([0.12600607, 0.90016997, 0.06664386, 0.10747039, 0.00279384,
+       0.82838037]), scaled_genome=None, log_likelihood=1025.2904419083861, fitness_values=array([0.06487392, 0.05296319, 0.0513765 , 0.05115109, 0.05111643,
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+       0.8283783 ]), scaled_genome=None, log_likelihood=1030.8335030348699, fitness_values=array([0.07575461, 0.05352142, 0.05154668, 0.05118416, 0.0511279 ,
+       0.0511176 , 0.05110813, 0.05110616, 0.051106  , 0.05110545]))]"
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+       0.82835304]), scaled_genome=None, log_likelihood=1027.1673158401945, fitness_values=array([0.06756633, 0.05161019, 0.05116222, 0.05112307, 0.05111293,
+       0.05110816, 0.05110566, 0.05110418, 0.05110256, 0.05110256])), Solution(fitness=0.051081842209423474, genome=array([0.12597394, 0.90062567, 0.06666565, 0.10745322, 0.00281642,
+       0.82833944]), scaled_genome=None, log_likelihood=1026.1183870904106, fitness_values=array([0.07768619, 0.05181798, 0.05119396, 0.05110411, 0.05110354,
+       0.05109139, 0.05108775, 0.05108443, 0.0510841 , 0.05108184])), Solution(fitness=0.05110527222375307, genome=array([0.12748069, 0.90153406, 0.06728251, 0.10746435, 0.00193451,
+       0.82840631]), scaled_genome=None, log_likelihood=1030.9137114151044, fitness_values=array([0.05866782, 0.05157434, 0.05114173, 0.05111326, 0.05110872,
+       0.05110623, 0.0511062 , 0.0511056 , 0.05110544, 0.05110527])), Solution(fitness=0.051079023756181355, genome=array([0.12599319, 0.90053644, 0.06659973, 0.10743835, 0.00275107,
+       0.8283238 ]), scaled_genome=None, log_likelihood=1025.9655864440901, fitness_values=array([0.05774943, 0.051466  , 0.05111425, 0.05110712, 0.05110109,
+       0.05109459, 0.05108837, 0.05108544, 0.05108025, 0.05107902])), Solution(fitness=0.05110534806364629, genome=array([0.12747553, 0.90129817, 0.06728134, 0.1074487 , 0.00193874,
+       0.82836684]), scaled_genome=None, log_likelihood=1030.4518205508466, fitness_values=array([0.08211433, 0.0527626 , 0.05118689, 0.05113754, 0.05111269,
+       0.05110918, 0.05110652, 0.05110582, 0.0511055 , 0.05110535])), Solution(fitness=0.05110536545257226, genome=array([0.12752859, 0.90143301, 0.06730661, 0.10745414, 0.0019082 ,
+       0.82838411]), scaled_genome=None, log_likelihood=1030.816398385739, fitness_values=array([0.06270932, 0.0514928 , 0.05116821, 0.0511238 , 0.05110975,
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+       0.8283899 ]), scaled_genome=None, log_likelihood=1030.9680984016277, fitness_values=array([0.07475074, 0.05168923, 0.05119217, 0.05112169, 0.05111217,
+       0.05110719, 0.05110656, 0.05110546, 0.05110538, 0.0511053 ])), Solution(fitness=0.051105339489600055, genome=array([0.12748024, 0.90136223, 0.06728263, 0.10746098, 0.00193845,
+       0.82839124]), scaled_genome=None, log_likelihood=1030.5827140254678, fitness_values=array([0.06705613, 0.0516628 , 0.05115816, 0.0511175 , 0.05110935,
+       0.05110741, 0.0511062 , 0.05110591, 0.05110547, 0.05110534])), Solution(fitness=0.05108166343180737, genome=array([0.12601843, 0.90106353, 0.06664137, 0.10745741, 0.00280986,
+       0.82839218]), scaled_genome=None, log_likelihood=1027.0451014941568, fitness_values=array([0.06707911, 0.05143806, 0.0511994 , 0.05110303, 0.05109976,
+       0.05109728, 0.05109133, 0.05108693, 0.05108245, 0.05108166])), Solution(fitness=0.05110537546964937, genome=array([0.12748001, 0.9013076 , 0.06728316, 0.10745727, 0.00193723,
+       0.82838265]), scaled_genome=None, log_likelihood=1030.4774288655642, fitness_values=array([0.06882062, 0.05180706, 0.05113222, 0.05112087, 0.05111093,
+       0.05110855, 0.05110659, 0.05110573, 0.05110548, 0.05110538]))]"
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+       0.82820898]), scaled_genome=None, log_likelihood=974.1413451398905, fitness_values=array([0.0793223 , 0.05537368, 0.05498012, 0.05475278, 0.0542923 ,
+       0.05282181, 0.05243086, 0.05218328, 0.05218328, 0.05218136])), Solution(fitness=0.05237255069165808, genome=array([0.12763136, 0.87236864, 0.06845581, 0.10657149, 0.00404704,
+       0.8291036 ]), scaled_genome=None, log_likelihood=973.9501011283202, fitness_values=array([0.07684366, 0.0523969 , 0.0523969 , 0.05237255, 0.05237255,
+       0.05237255, 0.05237255, 0.05237255, 0.05237255, 0.05237255])), Solution(fitness=0.05264336190625047, genome=array([1.26328377e-01, 8.73671623e-01, 6.63822675e-02, 1.08149345e-01,
+       8.45557130e-04, 8.29067304e-01]), scaled_genome=None, log_likelihood=973.3125536384892, fitness_values=array([0.09724451, 0.05785004, 0.05764783, 0.05536602, 0.05520649,
+       0.05295068, 0.05286547, 0.05267007, 0.05266931, 0.05264336])), Solution(fitness=0.05202611931126077, genome=array([0.12690724, 0.87309276, 0.06776389, 0.10677834, 0.00448225,
+       0.82625017]), scaled_genome=None, log_likelihood=973.9334629804989, fitness_values=array([0.07724056, 0.05393685, 0.05289754, 0.05235836, 0.05214001,
+       0.05212309, 0.0520369 , 0.0520271 , 0.05202612, 0.05202612])), Solution(fitness=0.05212402991406234, genome=array([0.1285711 , 0.8714289 , 0.06801015, 0.10754673, 0.0019581 ,
+       0.82722268]), scaled_genome=None, log_likelihood=973.9797126321005, fitness_values=array([0.0565393 , 0.0555646 , 0.05228774, 0.05221179, 0.0522016 ,
+       0.0522016 , 0.05215138, 0.05213095, 0.05212403, 0.05212403])), Solution(fitness=0.05215763480321571, genome=array([0.12384886, 0.87615114, 0.06839945, 0.10670135, 0.00443942,
+       0.82631134]), scaled_genome=None, log_likelihood=973.8088928999294, fitness_values=array([0.08180293, 0.05380016, 0.05281026, 0.05224498, 0.05216096,
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+       0.82666416]), scaled_genome=None, log_likelihood=973.9566958082723, fitness_values=array([0.05481349, 0.05301872, 0.05301872, 0.05301872, 0.05276949,
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+       0.82603556]), scaled_genome=None, log_likelihood=973.6007183262316, fitness_values=array([0.05570089, 0.05268149, 0.05233779, 0.05223279, 0.05223279,
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+       0.82845287]), scaled_genome=None, log_likelihood=973.9840944675478, fitness_values=array([0.0537605 , 0.05272279, 0.05249286, 0.0523981 , 0.05235859,
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+       0.826258  ]), scaled_genome=None, log_likelihood=973.7588668876466, fitness_values=array([0.07133755, 0.05578005, 0.05289418, 0.05232751, 0.05232444,
+       0.05227253, 0.05216807, 0.05209489, 0.05208865, 0.05207392]))]"
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+       0.82839368]), scaled_genome=None, log_likelihood=1030.6672936219338, fitness_values=array([0.09508839, 0.05456152, 0.05158754, 0.05112628, 0.05112628,
+       0.05110735, 0.05110535, 0.05110521, 0.0511052 , 0.0511052 ])), Solution(fitness=0.05107391724459467, genome=array([0.12603324, 0.90143554, 0.06658236, 0.10746727, 0.00272351,
+       0.82840527]), scaled_genome=None, log_likelihood=1027.7713873594862, fitness_values=array([0.07835475, 0.05779845, 0.05230237, 0.05122679, 0.05110701,
+       0.05108901, 0.05108062, 0.05107473, 0.05107392, 0.05107392])), Solution(fitness=0.05107392665688157, genome=array([0.12603297, 0.90138578, 0.06658255, 0.10746692, 0.00272365,
+       0.8284019 ]), scaled_genome=None, log_likelihood=1027.6750487103398, fitness_values=array([0.09862436, 0.06298948, 0.05175907, 0.05120499, 0.05112755,
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+       0.82839336]), scaled_genome=None, log_likelihood=1030.6102793801633, fitness_values=array([0.11596939, 0.05931121, 0.05168918, 0.05116463, 0.05112421,
+       0.05110737, 0.05110543, 0.05110523, 0.05110521, 0.05110521])), Solution(fitness=0.051105203604093104, genome=array([0.12747905, 0.90141932, 0.06728147, 0.10745986, 0.0019341 ,
+       0.82839205]), scaled_genome=None, log_likelihood=1030.689850535621, fitness_values=array([0.07999151, 0.05765443, 0.05187401, 0.05115036, 0.05111748,
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+       0.82839385]), scaled_genome=None, log_likelihood=1030.6452763274692, fitness_values=array([0.08816468, 0.05649791, 0.05163572, 0.0511891 , 0.05111743,
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+       0.82841085]), scaled_genome=None, log_likelihood=1027.6637244256617, fitness_values=array([0.12067153, 0.05452317, 0.05179774, 0.05117098, 0.05111665,
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+       0.82840034]), scaled_genome=None, log_likelihood=1027.9786743093255, fitness_values=array([0.09879788, 0.05741853, 0.05148224, 0.05117341, 0.05110101,
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+       -0.03673916,  0.09916   ,  0.61972171,  0.72098537,  0.98984834]), scaled_genome=None, log_likelihood=9515.68369188556, fitness_values=array([8.51447944, 2.38331157, 1.53862929, 1.40942358, 0.85333765,
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+       0.86704577, 0.77111526, 0.69146861, 0.59204382, 0.51940961]))]"
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