diff --git a/.gitignore b/.gitignore
index 0dd47dc6..b1a9cccb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -149,6 +149,9 @@ cython_debug/
 ### VSCODE ######################################################################
 .vscode
 .vscode/*
+.idea/*
+.idea
+.idea*
 # Local History for Visual Studio Code
 .history/
 # Built Visual Studio Code Extensions
@@ -263,3 +266,6 @@ RECYCLE.BIN/
 *.msp
 # Windows shortcuts
 *.lnk
+
+
+CLAUDE.md
diff --git a/docs/source/ea_intro/ea_example.ipynb b/docs/source/ea_intro/ea_example.ipynb
deleted file mode 100644
index 2b2e43d2..00000000
--- a/docs/source/ea_intro/ea_example.ipynb
+++ /dev/null
@@ -1,615 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "7f975200",
-   "metadata": {},
-   "source": [
-    "# This file shows the process of creating an EA using ARIEL "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c1a738e9",
-   "metadata": {},
-   "source": [
-    "### How does ARIEL EC module work?\n",
-    "\n",
-    "The ARIEL EC (Evolutionary Computing) module works a bit different than other EAs (Evolutionary Algorithms). While other EAs represent the population as a simple list of individuals, here they the population is made as its own class, with a population being type of `list[Individuals]`. \n",
-    "\n",
-    "Similarly, in traditional EA architecture an individual is chosen for certain operators (for example parent selection) according to some criteria, put into a separate list and then given to the operator function. ARIEL works by giving individuals what we call `tags`. An individual has `tags` that can be toggled, which qualify it for any and all operations. The tag can be whether an individual can crossover or mutate in the future, but it can also show if it can enter the learning cycle.\n",
-    "\n",
-    "The tags can be changed at all times, and default values for each tag can be given to more closely represent a traditional EA structure.\n",
-    "\n",
-    "Additionally, ARIEL utilizes an SQL database to handle the variables and outputs of the code. This makes the code run faster, but it adds an extra step to the process.\n",
-    "\n",
-    "This file demonstrates the process of initializing an EA class and running it for a simple problem, in our case, the Sphere function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8e608ecf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Standard library\n",
-    "import random\n",
-    "from typing import Literal, cast\n",
-    "\n",
-    "# Pretty little errors and progress bars\n",
-    "from rich.console import Console\n",
-    "from rich.traceback import install\n",
-    "\n",
-    "# Third-party libraries\n",
-    "import numpy as np\n",
-    "\n",
-    "# Local libraries\n",
-    "from ariel.ec.a000 import IntegerMutator\n",
-    "from ariel.ec.a001 import Individual\n",
-    "from ariel.ec.a005 import Crossover\n",
-    "from ariel.ec.a004 import EASettings, EAStep, EA, Population\n",
-    "\n",
-    "# Library to show fitness landscape\n",
-    "from fitness_plot import fitness_landscape_plot\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f90615ed",
-   "metadata": {},
-   "source": [
-    "#### Define fitness function"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "0bdef6b6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#### Define the fitness function\n",
-    "def Ackley(x):\n",
-    "    \"\"\"source: https://www.sfu.ca/~ssurjano/ackley.html\"\"\"\n",
-    "\n",
-    "    x = np.array(x)\n",
-    "    # Ackley function parameters\n",
-    "    a = 20\n",
-    "    b = 0.2\n",
-    "    c = 2 * np.pi\n",
-    "    dimension = len(x)\n",
-    "\n",
-    "    # Individual terms\n",
-    "    term1 = -a * np.exp(-b * np.sqrt(sum(x**2) / dimension))\n",
-    "    term2 = -np.exp(sum(np.cos(c * xi) for xi in x) / dimension)\n",
-    "    return term1 + term2 + a + np.exp(1)\n",
-    "\n",
-    "def evaluate_ind(ind: Individual) -> float:\n",
-    "    \"\"\"Evaluate an individual by calculating its fitness using the Ackley function.\"\"\"\n",
-    "\n",
-    "    return Ackley(cast(\"list[float]\", ind.genotype))\n",
-    "\n",
-    "def evaluate_pop(population: Population) -> Population:\n",
-    "    \"\"\"Evaluate a population by calculating the fitness of each individual.\"\"\"\n",
-    "    for ind in population:\n",
-    "        if ind.requires_eval:\n",
-    "            ind.fitness = evaluate_ind(ind)\n",
-    "    return population\n",
-    "\n",
-    "fitness_landscape_plot()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "46cc2ac5",
-   "metadata": {},
-   "source": [
-    "#### Initialize the global constants"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "72bb9020",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# A seed is optional, but it helps with reproducibility\n",
-    "SEED = None  # e.g., 42\n",
-    "\n",
-    "# The database has a few handling modes\n",
-    "    # \"delete\" will delete the existing database\n",
-    "    # \"halt\" will stop the execution if a database already exists\n",
-    "DB_HANDLING_MODES = Literal[\"delete\", \"halt\"]\n",
-    "\n",
-    "# Initialize RNG\n",
-    "RNG = np.random.default_rng(SEED)\n",
-    "\n",
-    "# Initialize rich console and traceback handler\n",
-    "install()\n",
-    "console = Console()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "96c38813",
-   "metadata": {},
-   "source": [
-    "### Initialize the EASettings class. \n",
-    "\n",
-    "The EASettings class acts as the handles the database and other parameters  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "2c598983",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set config\n",
-    "config = EASettings()\n",
-    "config.is_maximisation = False\n",
-    "config.db_handling = \"delete\"\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "985a5321",
-   "metadata": {},
-   "source": [
-    "#### And just like that we have everything we need to get started. Now all we need to do define our evolutionary operators\n",
-    "\n",
-    "Keep in mind that all operators in ARIEL have to work with the `Individual` and `Population` classes. You could define your own operators from scratch, but using the built in ones is easier."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "70b5cd55",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def create_individual(num_dims) -> Individual:\n",
-    "    ind = Individual()\n",
-    "    ind.genotype = cast(\"list[float]\", np.random.normal(loc=0, \n",
-    "                                                        scale=50, \n",
-    "                                                        size=num_dims).tolist())\n",
-    "    return ind\n",
-    "\n",
-    "def parent_selection(population: Population) -> Population:\n",
-    "    \"\"\"Tournament selection\"\"\"\n",
-    "    \n",
-    "    # Shuffle population to avoid bias\n",
-    "    random.shuffle(population)\n",
-    "\n",
-    "    # Tournament selection\n",
-    "    for idx in range(0, len(population) - 1, 2):\n",
-    "        ind_i = population[idx]\n",
-    "        ind_j = population[idx + 1]\n",
-    "\n",
-    "        # Compare fitness values and update tags\n",
-    "        if ind_i.fitness > ind_j.fitness and config.is_maximisation:\n",
-    "            ind_i.tags = {\"ps\": True}\n",
-    "            ind_j.tags = {\"ps\": False}\n",
-    "        else:\n",
-    "            ind_i.tags = {\"ps\": False}\n",
-    "            ind_j.tags = {\"ps\": True}\n",
-    "    return population\n",
-    "    \n",
-    "def crossover(population: Population) -> Population:\n",
-    "    \"\"\"One point crossover\"\"\"\n",
-    "\n",
-    "    parents = [ind for ind in population if ind.tags.get(\"ps\", False)]\n",
-    "    for idx in range(0, len(parents), 2):\n",
-    "        parent_i = parents[idx]\n",
-    "        parent_j = parents[idx]\n",
-    "        genotype_i, genotype_j = Crossover.one_point(\n",
-    "            cast(\"list[float]\", parent_i.genotype),\n",
-    "            cast(\"list[float]\", parent_j.genotype),\n",
-    "        )\n",
-    "\n",
-    "        # First child\n",
-    "        child_i = Individual()\n",
-    "        child_i.genotype = genotype_i\n",
-    "        child_i.tags = {\"mut\": True}\n",
-    "        child_i.requires_eval = True\n",
-    "\n",
-    "        # Second child\n",
-    "        child_j = Individual()\n",
-    "        child_j.genotype = genotype_j\n",
-    "        child_j.tags = {\"mut\": True}\n",
-    "        child_j.requires_eval = True\n",
-    "\n",
-    "        population.extend([child_i, child_j])\n",
-    "    return population\n",
-    "\n",
-    "def mutation(population: Population) -> Population:\n",
-    "    for ind in population:\n",
-    "        if ind.tags.get(\"mut\", False):\n",
-    "            genes = cast(\"list[int]\", ind.genotype)\n",
-    "            mutated = IntegerMutator.float_creep(\n",
-    "                individual=genes,\n",
-    "                span=5,\n",
-    "                mutation_probability=0.5,\n",
-    "            )\n",
-    "            ind.genotype = mutated\n",
-    "    return population\n",
-    "\n",
-    "def survivor_selection(population: Population) -> Population:\n",
-    "\n",
-    "    # Shuffle population to avoid bias\n",
-    "    random.shuffle(population)\n",
-    "    current_pop_size = len(population)\n",
-    "\n",
-    "    for idx in range(len(population)):\n",
-    "        ind_i = population[idx]\n",
-    "        ind_j = population[idx + 1]\n",
-    "\n",
-    "        # Kill worse individual\n",
-    "        if ind_i.fitness > ind_j.fitness and config.is_maximisation:\n",
-    "            ind_j.alive = False\n",
-    "        else:\n",
-    "            ind_i.alive = False\n",
-    "\n",
-    "        # Termination condition\n",
-    "        current_pop_size -= 1\n",
-    "        if current_pop_size <= config.target_population_size:\n",
-    "            break\n",
-    "    return population"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f02e88e3",
-   "metadata": {},
-   "source": [
-    "### Define evolutionary loop\n",
-    "\n",
-    "Now that all our operators are done, we can define the evolutionary loop and run the algorithm\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "8be92b97",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def main() -> EA:\n",
-    "    \"\"\"Entry point.\"\"\"\n",
-    "    # Create initial population\n",
-    "    population_list = [create_individual(num_dims=10) for _ in range(10)]\n",
-    "    print(population_list)\n",
-    "    population_list = evaluate_pop(population_list)\n",
-    "\n",
-    "    # Create EA steps\n",
-    "    ops = [\n",
-    "        EAStep(\"evaluation\", evaluate_pop),\n",
-    "        EAStep(\"parent_selection\", parent_selection),\n",
-    "        EAStep(\"crossover\", crossover),\n",
-    "        EAStep(\"mutation\", mutation),\n",
-    "        EAStep(\"evaluation\", evaluate_pop),\n",
-    "        EAStep(\"survivor_selection\", survivor_selection),\n",
-    "    ]\n",
-    "\n",
-    "    # Initialize EA\n",
-    "    ea = EA(\n",
-    "        population_list,\n",
-    "        operations=ops,\n",
-    "        num_of_generations=100,\n",
-    "    )\n",
-    "\n",
-    "    ea.run()\n",
-    "\n",
-    "    best = ea.get_solution(\"best\", only_alive=False)\n",
-    "    console.log(best)\n",
-    "\n",
-    "    median = ea.get_solution(\"median\", only_alive=False)\n",
-    "    console.log(median)\n",
-    "\n",
-    "    worst = ea.get_solution(\"worst\", only_alive=False)\n",
-    "    console.log(worst)\n",
-    "\n",
-    "    return ea"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "7428e0cf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[Individual(id=None, alive=True, time_of_birth=-1, time_of_death=-1, requires_eval=True, fitness_=None, requires_init=False, genotype_=[2.408246382881381, 5.79605190806094, -79.54098188412165, 15.722240082230273, 40.908873629938554, 23.655791886798323, -29.0401778456501, -44.0910910860608, 46.6994979834531, 44.51539693959488], tags_={}), Individual(id=None, alive=True, time_of_birth=-1, time_of_death=-1, requires_eval=True, fitness_=None, requires_init=False, genotype_=[38.220368939127326, 60.658833461353524, 55.66507395396577, 71.31449041051489, 90.68420709242794, -41.79576370072132, -48.79004377864702, 31.46344668265647, -48.003370589026225, 14.02324800551763], tags_={}), Individual(id=None, alive=True, time_of_birth=-1, time_of_death=-1, requires_eval=True, fitness_=None, requires_init=False, genotype_=[-32.90740512919547, 25.036141835548538, 0.36711466327349074, 53.76353799300189, -38.53125411144, 5.35745175330088, -23.019156012124697, -66.337062441846, -50.75845026113798, 48.871897267390615], tags_={}), Individual(id=None, alive=True, time_of_birth=-1, time_of_death=-1, requires_eval=True, fitness_=None, requires_init=False, genotype_=[8.480801108291654, 37.324015811202074, 13.77265902980842, -1.674551608353941, 33.60714493740689, -68.04372634235611, -21.07910761451974, 90.43188951031527, 40.810541611687874, -41.991014937738484], tags_={}), Individual(id=None, alive=True, time_of_birth=-1, time_of_death=-1, requires_eval=True, fitness_=None, requires_init=False, genotype_=[22.598011128899692, 81.21670228397613, 107.72432244657797, 62.451789603804855, 4.115812806901929, -22.829906859778426, -95.05978577286338, -21.393570976959076, 54.62456102918504, 56.26303001018417], tags_={}), Individual(id=None, alive=True, time_of_birth=-1, time_of_death=-1, requires_eval=True, fitness_=None, requires_init=False, genotype_=[-74.90248116978553, -11.608668709580524, -6.543964231789149, -13.846612344646648, 51.83963355511509, 14.54237506248581, -81.0733495761288, 77.61737393510838, -7.5836843180492775, -0.4067953373474761], tags_={}), Individual(id=None, alive=True, time_of_birth=-1, time_of_death=-1, requires_eval=True, fitness_=None, requires_init=False, genotype_=[-67.80960209698705, 33.39780118847392, 32.55670675817498, -16.403100152228347, -9.433215961739206, 22.568662989164785, -30.05464476440501, -41.711418138880255, 55.80315469671937, -13.910639063186434], tags_={}), Individual(id=None, alive=True, time_of_birth=-1, time_of_death=-1, requires_eval=True, fitness_=None, requires_init=False, genotype_=[-33.06434449432155, 52.54747489946278, -44.17130149134966, 115.0643332294387, 32.58912362478761, 64.33393127521214, 16.71974489005757, 36.90015504392898, -54.005293408302826, -9.982137689745969], tags_={}), Individual(id=None, alive=True, time_of_birth=-1, time_of_death=-1, requires_eval=True, fitness_=None, requires_init=False, genotype_=[-24.170818363594336, -33.78692216610353, -30.895768744016184, 41.402978496168906, -15.979658595357071, 48.43945857621487, 15.745167596368304, -73.02747531014909, 66.7679632217159, -69.10790924577002], tags_={}), Individual(id=None, alive=True, time_of_birth=-1, time_of_death=-1, requires_eval=True, fitness_=None, requires_init=False, genotype_=[-79.3089113486005, 20.924907957331843, 76.49876452092195, -3.2991870397473755, 110.76415118349598, 83.50069159571638, -43.16998478652831, 4.037131419505667, 12.567409545787934, -31.844919779373075], tags_={})]\n"
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-       "\u001b[92m───────────────────────────────────────────────── \u001b[0m\u001b[34mEA Initialised\u001b[0m\u001b[92m ──────────────────────────────────────────────────\u001b[0m\n"
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-       "Output()"
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-       "\u001b[92m─────────────────────────────────────────────── \u001b[0m\u001b[32mEA Finished Running\u001b[0m\u001b[92m ───────────────────────────────────────────────\u001b[0m\n"
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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[15:05:25] </span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Individual</span><span style=\"font-weight: bold\">(</span>                                                                             <a href=\"file://C:\\Users\\johng\\AppData\\Local\\Temp\\ipykernel_13864\\2235138345.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2235138345.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file://C:\\Users\\johng\\AppData\\Local\\Temp\\ipykernel_13864\\2235138345.py#28\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">28</span></a>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>    <span style=\"color: #808000; text-decoration-color: #808000\">time_of_birth</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">53</span>,                                                                   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>    <span style=\"color: #808000; text-decoration-color: #808000\">alive</span>=<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span>,                                                                        <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>    <span style=\"color: #808000; text-decoration-color: #808000\">id</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2441</span>,                                                                            <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>    <span style=\"color: #808000; text-decoration-color: #808000\">requires_eval</span>=<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span>,                                                                <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>    <span style=\"color: #808000; text-decoration-color: #808000\">requires_init</span>=<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span>,                                                                <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>    <span style=\"color: #808000; text-decoration-color: #808000\">tags_</span>=<span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'mut'</span>: <span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'ps'</span>: <span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span><span style=\"font-weight: bold\">}</span>,                                                   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>    <span style=\"color: #808000; text-decoration-color: #808000\">time_of_death</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">55</span>,                                                                   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>    <span style=\"color: #808000; text-decoration-color: #808000\">fitness_</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">19.943074283612287</span>,                                                        <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>    <span style=\"color: #808000; text-decoration-color: #808000\">genotype_</span>=<span style=\"font-weight: bold\">[</span>                                                                         <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-44.349598792593326</span>,                                                            <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22.71370375509321</span>,                                                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">41.98038192155346</span>,                                                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-23.57200595364493</span>,                                                             <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-4.0</span>,                                                                           <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">9.710941958036557</span>,                                                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-15.0</span>,                                                                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-29.219839593502666</span>,                                                            <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">45.0</span>,                                                                           <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-3.596580788360625</span>                                                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>    <span style=\"font-weight: bold\">]</span>                                                                                   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"font-weight: bold\">)</span>                                                                                       <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "</pre>\n"
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-       "\u001b[2;36m[15:05:25]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;35mIndividual\u001b[0m\u001b[1m(\u001b[0m                                                                             \u001b]8;id=907311;file://C:\\Users\\johng\\AppData\\Local\\Temp\\ipykernel_13864\\2235138345.py\u001b\\\u001b[2m2235138345.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=763723;file://C:\\Users\\johng\\AppData\\Local\\Temp\\ipykernel_13864\\2235138345.py#28\u001b\\\u001b[2m28\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[2;36m           \u001b[0m    \u001b[33mtime_of_birth\u001b[0m=\u001b[1;36m53\u001b[0m,                                                                   \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m    \u001b[33malive\u001b[0m=\u001b[3;91mFalse\u001b[0m,                                                                        \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m    \u001b[33mid\u001b[0m=\u001b[1;36m2441\u001b[0m,                                                                            \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m    \u001b[33mrequires_eval\u001b[0m=\u001b[3;91mFalse\u001b[0m,                                                                \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m    \u001b[33mrequires_init\u001b[0m=\u001b[3;91mFalse\u001b[0m,                                                                \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m    \u001b[33mtags_\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'mut'\u001b[0m: \u001b[3;92mTrue\u001b[0m, \u001b[32m'ps'\u001b[0m: \u001b[3;91mFalse\u001b[0m\u001b[1m}\u001b[0m,                                                   \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m    \u001b[33mtime_of_death\u001b[0m=\u001b[1;36m55\u001b[0m,                                                                   \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m    \u001b[33mfitness_\u001b[0m=\u001b[1;36m19\u001b[0m\u001b[1;36m.943074283612287\u001b[0m,                                                        \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m    \u001b[33mgenotype_\u001b[0m=\u001b[1m[\u001b[0m                                                                         \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m        \u001b[1;36m-44.349598792593326\u001b[0m,                                                            \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m        \u001b[1;36m22.71370375509321\u001b[0m,                                                              \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m        \u001b[1;36m41.98038192155346\u001b[0m,                                                              \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m        \u001b[1;36m-23.57200595364493\u001b[0m,                                                             \u001b[2m                \u001b[0m\n",
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-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"font-weight: bold\">)</span>                                                                                       <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
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-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">74.18702515362334</span>,                                                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-64.28798233024189</span>                                                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>    <span style=\"font-weight: bold\">]</span>                                                                                   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"font-weight: bold\">)</span>                                                                                       <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;35mIndividual\u001b[0m\u001b[1m(\u001b[0m                                                                             \u001b]8;id=605709;file://C:\\Users\\johng\\AppData\\Local\\Temp\\ipykernel_13864\\2235138345.py\u001b\\\u001b[2m2235138345.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=622651;file://C:\\Users\\johng\\AppData\\Local\\Temp\\ipykernel_13864\\2235138345.py#34\u001b\\\u001b[2m34\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[2;36m           \u001b[0m    \u001b[33mtime_of_birth\u001b[0m=\u001b[1;36m60\u001b[0m,                                                                   \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m    \u001b[33malive\u001b[0m=\u001b[3;91mFalse\u001b[0m,                                                                        \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m    \u001b[33mid\u001b[0m=\u001b[1;36m2817\u001b[0m,                                                                            \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m    \u001b[33mrequires_eval\u001b[0m=\u001b[3;91mFalse\u001b[0m,                                                                \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m    \u001b[33mrequires_init\u001b[0m=\u001b[3;91mFalse\u001b[0m,                                                                \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m    \u001b[33mtags_\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'mut'\u001b[0m: \u001b[3;92mTrue\u001b[0m, \u001b[32m'ps'\u001b[0m: \u001b[3;91mFalse\u001b[0m\u001b[1m}\u001b[0m,                                                   \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m    \u001b[33mtime_of_death\u001b[0m=\u001b[1;36m61\u001b[0m,                                                                   \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m    \u001b[33mfitness_\u001b[0m=\u001b[1;36m22\u001b[0m\u001b[1;36m.007885345384366\u001b[0m,                                                        \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m    \u001b[33mgenotype_\u001b[0m=\u001b[1m[\u001b[0m                                                                         \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m        \u001b[1;36m-38.43861042164279\u001b[0m,                                                             \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m        \u001b[1;36m-25.580955959432835\u001b[0m,                                                            \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m        \u001b[1;36m-11.373770419240499\u001b[0m,                                                            \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m        \u001b[1;36m50.55614494636733\u001b[0m,                                                              \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m        \u001b[1;36m1.7959731101461838\u001b[0m,                                                             \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m        \u001b[1;36m60.154819324843714\u001b[0m,                                                             \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m        \u001b[1;36m19.764137429216152\u001b[0m,                                                             \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m        \u001b[1;36m-43.37437022761751\u001b[0m,                                                             \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m        \u001b[1;36m74.18702515362334\u001b[0m,                                                              \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m        \u001b[1;36m-64.28798233024189\u001b[0m                                                              \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m    \u001b[1m]\u001b[0m                                                                                   \u001b[2m                \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m\u001b[1m)\u001b[0m                                                                                       \u001b[2m                \u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ea = main()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ea9c19d0",
-   "metadata": {},
-   "source": [
-    "## Work in progress \n",
-    "\n",
-    "### Will update with the plot showing the mean performance and standard deviation of the algorithm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4d698940",
-   "metadata": {},
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "ariel",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/.DS_Store b/examples/.DS_Store
index 5008ddfc..0b73d529 100644
Binary files a/examples/.DS_Store and b/examples/.DS_Store differ
diff --git a/examples/0_render_single_frame.py b/examples/0_render_single_frame.py
index 1f1af790..4d0577c2 100644
--- a/examples/0_render_single_frame.py
+++ b/examples/0_render_single_frame.py
@@ -38,7 +38,7 @@ def main() -> None:
     data = mujoco.MjData(model)
 
     # Render a single frame
-    single_frame_renderer(model, data, steps=10_000)
+    single_frame_renderer(model, data, steps=10_000, save=True)
 
 
 if __name__ == "__main__":
diff --git a/examples/__init__.py b/examples/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/examples/_display_robot_from_json.py b/examples/_display_robot_from_json.py
new file mode 100644
index 00000000..98f72228
--- /dev/null
+++ b/examples/_display_robot_from_json.py
@@ -0,0 +1,133 @@
+"""TODO(jmdm): description of script.
+
+Author:     jmdm
+Date:       2025-06-25
+Py Ver:     3.12
+OS:         macOS  Sequoia 15.3.1
+Hardware:   M4 Pro
+Status:     Completed ✅
+"""
+
+# Standard library
+from pathlib import Path
+from typing import TYPE_CHECKING, Any
+
+# Third-party libraries
+import mujoco
+import numpy as np
+from mujoco import viewer
+from rich.console import Console
+
+# Local libraries
+from ariel.body_phenotypes.robogen_lite.config import (
+    NUM_OF_FACES,
+    NUM_OF_ROTATIONS,
+    NUM_OF_TYPES_OF_MODULES,
+)
+from ariel.body_phenotypes.robogen_lite.constructor import (
+    construct_mjspec_from_graph,
+)
+from ariel.body_phenotypes.robogen_lite.decoders.l_system_genotype import (
+    load_graph_from_json,
+)
+
+
+from ariel.body_phenotypes.robogen_lite.modules.core import CoreModule
+from ariel.simulation.environments import SimpleFlatWorld
+from ariel.utils.renderers import single_frame_renderer
+
+if TYPE_CHECKING:
+    from networkx import DiGraph
+
+
+# Global constants
+SCRIPT_NAME = __file__.split("/")[-1][:-3]
+CWD = Path.cwd()
+DATA = Path(CWD / "__data__" / SCRIPT_NAME)
+DATA.mkdir(exist_ok=True)
+SEED = 40
+
+# Global functions
+console = Console()
+RNG = np.random.default_rng(SEED)
+
+
+def main() -> None:
+    """Entry point."""
+
+    graph_parent1 = load_graph_from_json("parent1.json")
+    graph_parent2 = load_graph_from_json("parent2.json")
+    graph_parent1_mutated = load_graph_from_json("parent1-mutated.json")
+    graph_parent2_mutated = load_graph_from_json("parent2-mutated.json")
+    graph_offspring1 = load_graph_from_json("offspring1.json")
+    graph_offspring2 = load_graph_from_json("offspring2.json")
+    graph_offspring3 = load_graph_from_json("offspring3.json")
+    graph_offspring4 = load_graph_from_json("offspring4.json")
+
+    # Construct the robot from the graph
+    core_parent1 = construct_mjspec_from_graph(graph_parent1)
+    core_parent2 = construct_mjspec_from_graph(graph_parent2)
+    core_parent1_mutated = construct_mjspec_from_graph(graph_parent1_mutated)
+    core_parent2_mutated = construct_mjspec_from_graph(graph_parent2_mutated)
+    core_offspring1 = construct_mjspec_from_graph(graph_offspring1)
+    core_offspring2 = construct_mjspec_from_graph(graph_offspring2)
+    core_offspring3 = construct_mjspec_from_graph(graph_offspring3)
+    core_offspring4 = construct_mjspec_from_graph(graph_offspring4)
+    # Simulate the robot
+    run(core_parent1, with_viewer=True)
+    run(core_parent2, with_viewer=True)
+    run(core_parent1_mutated, with_viewer=True)
+    run(core_parent2_mutated, with_viewer=True)
+    run(core_offspring1, with_viewer=True)
+    run(core_offspring2, with_viewer=True)
+    run(core_offspring3, with_viewer=True)
+    run(core_offspring4, with_viewer=True)
+
+def run(
+    robot: CoreModule,
+    *,
+    with_viewer: bool = False,
+) -> None:
+    """Entry point."""
+    # MuJoCo configuration
+    viz_options = mujoco.MjvOption()  # visualization of various elements
+
+    # Visualization of the corresponding model or decoration element
+    viz_options.flags[mujoco.mjtVisFlag.mjVIS_TRANSPARENT] = True
+    viz_options.flags[mujoco.mjtVisFlag.mjVIS_ACTUATOR] = True
+    viz_options.flags[mujoco.mjtVisFlag.mjVIS_BODYBVH] = True
+
+    # MuJoCo basics
+    world = SimpleFlatWorld()
+
+    # Set random colors for geoms
+    for i in range(len(robot.spec.geoms)):
+        robot.spec.geoms[i].rgba[-1] = 0.5
+    # Spawn the robot at the world
+    world.spawn(robot.spec)
+
+    # Compile the model
+    model = world.spec.compile()
+    data = mujoco.MjData(model)
+
+    # Save the model to XML
+    xml = world.spec.to_xml()
+    with (DATA / f"{SCRIPT_NAME}.xml").open("w", encoding="utf-8") as f:
+        f.write(xml)
+
+    # Number of actuators and DoFs
+    console.log(f"DoF (model.nv): {model.nv}, Actuators (model.nu): {model.nu}")
+
+    # Reset state and time of simulation
+    mujoco.mj_resetData(model, data)
+
+    # Render
+    single_frame_renderer(model, data, steps=10)
+
+    # View
+    if with_viewer:
+        viewer.launch(model=model, data=data)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/_graph_to_robot.py b/examples/_graph_to_robot.py
index 1ba2690f..7b457097 100644
--- a/examples/_graph_to_robot.py
+++ b/examples/_graph_to_robot.py
@@ -31,6 +31,7 @@
     HighProbabilityDecoder,
     save_graph_as_json,
 )
+#from ariel.body_phenotypes.robogen_lite.morphology import Morphology
 from ariel.body_phenotypes.robogen_lite.modules.core import CoreModule
 from ariel.simulation.environments import SimpleFlatWorld
 from ariel.utils.renderers import single_frame_renderer
diff --git a/examples/config.toml b/examples/config.toml
new file mode 100644
index 00000000..317cf00e
--- /dev/null
+++ b/examples/config.toml
@@ -0,0 +1,70 @@
+# =========================
+# Evolutionary Algorithm — config.toml
+# =========================
+
+[run]
+# Choose which genotype profile to use for this run.
+# One of: "integers", "tree", "lsystem", "cppn"
+genotype = "lsystem"
+
+task = "evolve_to_copy"
+
+[task.evolve_to_copy]
+target_robot_path = "examples/target_robots/small_robot_8.json"
+
+# =========================
+# Global EC settings (match EASettings fields)
+# =========================
+[ec]
+quiet = false
+is_maximisation = true
+first_generation_id = 0
+num_of_generations = 100
+target_population_size = 100
+
+# =========================
+# Data config
+# =========================
+[data]
+# Paths can be absolute or relative to the working directory
+output_folder = "__data__"
+db_file_name = "database.db"
+# db_handling modes: "delete" | "reuse" | "migrate" (adjust to your app’s enum)
+db_handling = "delete"
+
+# =========================
+# Genotype registry
+# Each genotype declares:
+# - allowed_mutations / allowed_crossovers: for validation/help
+# - defaults: operators used if not overridden in [run]
+# - (Optional) operator params (e.g., rates, alphas)
+# =========================
+
+[genotypes.tree]
+allowed_mutations  = ["random_subtree_replacement"]
+allowed_crossovers = ["koza_default", "normal"]
+
+[genotypes.tree.defaults]
+mutation = "random_subtree_replacement"
+crossover = "koza_default"
+
+[genotypes.tree.mutation.params]
+max_subtree_depth = 2
+branching_prob = 0.5
+
+[genotypes.tree.crossover.params]
+koza_internal_node_prob = 0.9
+
+[genotypes.lsystem]
+allowed_mutations   = ["mutate_one_point_lsystem"]
+allowed_crossovers  = ["crossover_uniform_rules_lsystem", "crossover_uniform_genes_lsystem"]
+
+[genotypes.lsystem.defaults]
+mutation = "mutate_one_point_lsystem"
+crossover = "crossover_uniform_rules_lsystem"
+
+[genotypes.lsystem.mutation.params]
+mutation_rate = 0.5
+
+[genotypes.lsystem.crossover.params]
+mutation_rate = 0.5
diff --git a/examples/evolution_dashboard.py b/examples/evolution_dashboard.py
new file mode 100644
index 00000000..bab50824
--- /dev/null
+++ b/examples/evolution_dashboard.py
@@ -0,0 +1,267 @@
+#!/usr/bin/env python3
+"""
+Interactive Evolution Dashboard
+
+This module provides an interactive web dashboard for visualizing evolutionary
+computation results using Plotly Dash. It displays morphological analysis
+plots from MorphologyAnalyzer with generation selection capabilities.
+"""
+
+import dash
+from dash import dcc, html, Input, Output, callback
+import plotly.graph_objects as go
+import numpy as np
+import pandas as pd
+from typing import List, Callable, Any
+
+from plotly_morphology_analysis import PlotlyMorphologyAnalyzer
+from ariel.ec.a001 import Individual
+
+type Population = List[Individual]
+
+
+class EvolutionDashboard:
+    """Interactive dashboard for evolution visualization."""
+    
+    def __init__(self, populations: List[Population], decoder: Callable, config: Any):
+        """Initialize dashboard with evolution data.
+        
+        Args:
+            populations: List of populations per generation
+            decoder: Function to decode Individual genotype to robot graph
+            config: Evolution configuration object
+        """
+        self.populations = populations
+        self.decoder = decoder
+        self.config = config
+        self.analyzer = PlotlyMorphologyAnalyzer()
+        
+        # Load target robots
+        if hasattr(config, 'target_robot_file_path') and config.target_robot_file_path:
+            self.analyzer.load_target_robots(str(config.target_robot_file_path))
+        
+        # Pre-compute fitness timeline
+        self._compute_fitness_timeline()
+        
+        # Cache for computed descriptors per generation
+        self._descriptor_cache = {}
+        
+        # Initialize Dash app
+        self.app = dash.Dash(__name__)
+        self._setup_layout()
+        self._setup_callbacks()
+    
+    def _compute_fitness_timeline(self):
+        """Compute average fitness for each generation."""
+        self.fitness_timeline = []
+        
+        for gen_idx, population in enumerate(self.populations):
+            if population:
+                avg_fitness = sum(ind.fitness for ind in population) / len(population)
+                self.fitness_timeline.append({
+                    'generation': gen_idx,
+                    'avg_fitness': avg_fitness,
+                    'best_fitness': max(ind.fitness for ind in population),
+                    'worst_fitness': min(ind.fitness for ind in population)
+                })
+    
+    def _get_generation_data(self, generation: int):
+        """Get or compute morphological data for a specific generation."""
+        if generation in self._descriptor_cache:
+            return self._descriptor_cache[generation]
+        
+        if generation >= len(self.populations):
+            generation = len(self.populations) - 1
+        
+        # Load population data into analyzer
+        population = self.populations[generation]
+        self.analyzer.load_population(population, self.decoder)
+        self.analyzer.compute_fitness_scores()
+        
+        # Cache the results
+        self._descriptor_cache[generation] = {
+            'descriptors': self.analyzer.descriptors.copy(),
+            'fitness_scores': self.analyzer.fitness_scores.copy()
+        }
+        
+        return self._descriptor_cache[generation]
+    
+    def _setup_layout(self):
+        """Setup the dashboard layout."""
+        max_generation = len(self.populations) - 1
+        
+        self.app.layout = html.Div([
+            html.H1("Evolution Dashboard", style={'textAlign': 'center', 'marginBottom': 30}),
+            
+            # Generation control section
+            html.Div([
+                html.Label("Select Generation:", style={'fontWeight': 'bold', 'marginBottom': 10}),
+                dcc.Slider(
+                    id='generation-slider',
+                    min=0,
+                    max=max_generation,
+                    step=1,
+                    value=max_generation,
+                    marks={i: str(i) for i in range(0, max_generation + 1, max(1, max_generation // 10))},
+                    tooltip={"placement": "bottom", "always_visible": True}
+                )
+            ], style={'margin': '20px', 'marginBottom': 40}),
+            
+            # Fitness timeline (always visible)
+            html.Div([
+                html.H3("Fitness Over Generations"),
+                dcc.Graph(id='fitness-timeline')
+            ], style={'margin': '20px', 'marginBottom': 40}),
+            
+            # Tabbed plots section
+            html.Div([
+                dcc.Tabs(id='plot-tabs', value='pca-tab', children=[
+                    dcc.Tab(label='PCA Analysis', value='pca-tab'),
+                    dcc.Tab(label='Fitness Landscapes', value='landscape-tab'),
+                    dcc.Tab(label='Fitness Distributions', value='distribution-tab'),
+                    dcc.Tab(label='Morphological Diversity', value='diversity-tab'),
+                    dcc.Tab(label='Pairwise Features', value='pairwise-tab'),
+                ]),
+                html.Div(id='tab-content')
+            ], style={'margin': '20px'})
+        ])
+    
+    def _setup_callbacks(self):
+        """Setup dashboard callbacks."""
+        
+        @self.app.callback(
+            Output('fitness-timeline', 'figure'),
+            Input('generation-slider', 'value')
+        )
+        def update_fitness_timeline(selected_generation):
+            """Update fitness timeline with highlighted generation."""
+            if not self.fitness_timeline:
+                return go.Figure()
+            
+            df = pd.DataFrame(self.fitness_timeline)
+            
+            fig = go.Figure()
+            
+            # Add average fitness line
+            fig.add_trace(go.Scatter(
+                x=df['generation'],
+                y=df['avg_fitness'],
+                mode='lines+markers',
+                name='Average Fitness',
+                line=dict(color='blue', width=2)
+            ))
+            
+            # Add best fitness line
+            fig.add_trace(go.Scatter(
+                x=df['generation'],
+                y=df['best_fitness'],
+                mode='lines+markers',
+                name='Best Fitness',
+                line=dict(color='green', width=2)
+            ))
+            
+            # Highlight selected generation
+            if selected_generation < len(df):
+                selected_row = df.iloc[selected_generation]
+                fig.add_trace(go.Scatter(
+                    x=[selected_row['generation']],
+                    y=[selected_row['avg_fitness']],
+                    mode='markers',
+                    name=f'Generation {selected_generation}',
+                    marker=dict(color='red', size=12, symbol='circle-open', line=dict(width=3))
+                ))
+            
+            fig.update_layout(
+                title='Fitness Evolution Over Generations',
+                xaxis_title='Generation',
+                yaxis_title='Fitness',
+                height=400,
+                showlegend=True
+            )
+            
+            return fig
+        
+        @self.app.callback(
+            Output('tab-content', 'children'),
+            [Input('plot-tabs', 'value'),
+             Input('generation-slider', 'value')]
+        )
+        def update_tab_content(active_tab, selected_generation):
+            """Update tab content based on selection."""
+            # Get data for selected generation
+            gen_data = self._get_generation_data(selected_generation)
+            
+            if active_tab == 'pca-tab':
+                return self._create_pca_plot(selected_generation)
+            elif active_tab == 'landscape-tab':
+                return self._create_landscape_plot(selected_generation)
+            elif active_tab == 'distribution-tab':
+                return self._create_distribution_plot(selected_generation)
+            elif active_tab == 'diversity-tab':
+                return self._create_diversity_plot(selected_generation)
+            elif active_tab == 'pairwise-tab':
+                return self._create_pairwise_plot(selected_generation)
+            
+            return html.Div("Select a tab to view plots")
+    
+    def _create_pca_plot(self, generation: int):
+        """Create PCA analysis plot using PlotlyMorphologyAnalyzer."""
+        self._get_generation_data(generation)  # Load data into analyzer
+        fig = self.analyzer.plot_target_descriptors_pca()
+        fig.update_layout(title_text=f"PCA Analysis - Generation {generation}")
+        return dcc.Graph(figure=fig)
+    
+    def _create_landscape_plot(self, generation: int):
+        """Create fitness landscape plot using PlotlyMorphologyAnalyzer."""
+        self._get_generation_data(generation)  # Load data into analyzer
+        fig = self.analyzer.plot_fitness_landscapes()
+        fig.update_layout(title_text=f"Fitness Landscapes - Generation {generation}")
+        return dcc.Graph(figure=fig)
+    
+    def _create_distribution_plot(self, generation: int):
+        """Create fitness distribution plot using PlotlyMorphologyAnalyzer."""
+        self._get_generation_data(generation)  # Load data into analyzer
+        fig = self.analyzer.plot_fitness_distributions()
+        fig.update_layout(title_text=f"Fitness Distributions - Generation {generation}")
+        return dcc.Graph(figure=fig)
+    
+    def _create_diversity_plot(self, generation: int):
+        """Create morphological diversity analysis plot using PlotlyMorphologyAnalyzer."""
+        self._get_generation_data(generation)  # Load data into analyzer
+        fig = self.analyzer.analyze_morphological_diversity()
+        fig.update_layout(title_text=f"Morphological Diversity - Generation {generation}")
+        return dcc.Graph(figure=fig)
+    
+    def _create_pairwise_plot(self, generation: int):
+        """Create pairwise feature landscape plot using PlotlyMorphologyAnalyzer."""
+        self._get_generation_data(generation)  # Load data into analyzer
+        fig = self.analyzer.plot_pairwise_feature_landscapes()
+        fig.update_layout(title_text=f"Pairwise Feature Landscapes - Generation {generation}")
+        return dcc.Graph(figure=fig)
+    
+    def run(self, host='127.0.0.1', port=8050, debug=True):
+        """Run the dashboard server."""
+        print(f"Starting Evolution Dashboard at http://{host}:{port}")
+        print("Press Ctrl+C to stop the server")
+        self.app.run(host=host, port=port, debug=debug)
+
+
+def run_dashboard(populations: List[Population], decoder: Callable, config: Any, 
+                 host='127.0.0.1', port=8050, debug=True):
+    """Run the evolution dashboard.
+    
+    Args:
+        populations: List of populations per generation from evolution
+        decoder: Function to decode Individual genotype to robot graph  
+        config: Evolution configuration object
+        host: Server host address
+        port: Server port
+        debug: Enable debug mode
+    """
+    dashboard = EvolutionDashboard(populations, decoder, config)
+    dashboard.run(host=host, port=port, debug=debug)
+
+
+if __name__ == "__main__":
+    # Example usage - this would be called from evolve.py
+    print("Evolution Dashboard module - import and call run_dashboard() with your evolution data")
\ No newline at end of file
diff --git a/examples/evolve.py b/examples/evolve.py
new file mode 100644
index 00000000..abaa29fe
--- /dev/null
+++ b/examples/evolve.py
@@ -0,0 +1,296 @@
+# Standard library
+from __future__ import annotations
+import random
+from collections.abc import Callable
+from dataclasses import dataclass
+from pathlib import Path
+import tomllib
+from typing import Literal, cast, TYPE_CHECKING
+from functools import partial
+import matplotlib.pyplot as plt
+
+# Third-party libraries
+import numpy as np
+from pydantic_settings import BaseSettings
+from rich.console import Console
+from rich.traceback import install
+
+# Local libraries
+from ariel.ec.a001 import Individual
+from ariel.ec.a004 import EAStep, EA
+from ariel.ec.mutations import Mutation
+from ariel.ec.crossovers import Crossover
+from ariel.ec.genotypes.genotype_mapping import GENOTYPES_MAPPING
+from morphology_fitness_analysis import compute_6d_descriptor, load_target_robot, compute_fitness_scores, MorphologyAnalyzer
+from evolution_dashboard import run_dashboard
+
+from ariel.ec.genotypes.genotype import Genotype
+
+# Global constants
+SEED = 42
+DB_HANDLING_MODES = Literal["delete", "halt"]
+
+# Global functions
+install()
+console = Console()
+RNG = np.random.default_rng(SEED)
+
+# Type Aliases
+type Population = list[Individual]
+type PopulationFunc = Callable[[Population], Population]
+
+class EASettings(BaseSettings):
+    quiet: bool = False
+
+    # EC mechanisms
+    is_maximisation: bool = True
+    first_generation_id: int = 0
+    num_of_generations: int = 100
+    target_population_size: int = 100
+    genotype: type[Genotype]
+    mutation: Mutation
+    mutation_params: dict = {}
+    crossover: Crossover
+    crossover_params: dict = {}
+
+    task: str = "evolve_to_copy"
+    target_robot_file_path: Path | None = Path("examples/target_robots/small_robot_8.json")
+
+    # Data config
+    output_folder: Path = Path.cwd() / "__data__"
+    db_file_name: str = "database.db"
+    db_file_path: Path = output_folder / db_file_name
+    db_handling: DB_HANDLING_MODES = "delete"
+
+# ------------------------ EA STEPS ------------------------ #
+def parent_selection(population: Population, config: EASettings) -> Population:
+    random.shuffle(population)
+    for idx in range(0, len(population) - 1, 2):
+        ind_i = population[idx]
+        ind_j = population[idx + 1]
+
+        # Compare fitness values
+        if ind_i.fitness > ind_j.fitness and config.is_maximisation:
+            ind_i.tags = {"ps": True}
+            ind_j.tags = {"ps": False}
+        else:
+            ind_i.tags = {"ps": False}
+            ind_j.tags = {"ps": True}
+    return population
+
+
+def crossover(population: Population, config: EASettings) -> Population:
+    parents = [ind for ind in population if ind.tags.get("ps", False)]
+    for idx in range(0, len(parents)-1, 2):
+        parent_i = parents[idx]
+        parent_j = parents[idx + 1]
+        genotype_i, genotype_j = config.crossover(
+            config.genotype.from_json(parent_i.genotype),
+            config.genotype.from_json(parent_j.genotype),
+            **config.crossover_params,
+        )
+
+        # First child
+        child_i = Individual()
+        child_i.genotype = config.genotype.to_json(genotype_i)
+        child_i.tags = {"mut": True}
+        child_i.requires_eval = True
+
+        # Second child
+        child_j = Individual()
+        child_j.genotype = config.genotype.to_json(genotype_j)
+        child_j.tags = {"mut": True}
+        child_j.requires_eval = True
+
+        population.extend([child_i, child_j])
+    return population
+
+
+def mutation(population: Population, config: EASettings) -> Population:
+    for ind in population:
+        if ind.tags.get("mut", False):
+            genes = config.genotype.from_json(ind.genotype)
+            mutated = config.mutation(
+                individual=genes,
+                **config.mutation_params,
+            )
+            ind.genotype = config.genotype.to_json(mutated)
+            ind.requires_eval = True
+    return population
+
+
+def evaluate(population: Population, config: EASettings) -> Population:
+    if config.task == "evolve_to_copy":
+        target_descriptor = load_target_robot(Path(str(config.target_robot_file_path)))
+
+        for ind in population:
+            genotype = config.genotype.from_json(ind.genotype)
+            # Convert to digraph
+            ind_digraph = genotype.to_digraph(genotype)
+            # Compute the morphological descriptors
+            measures = compute_6d_descriptor(ind_digraph)
+            fitness = compute_fitness_scores(target_descriptor, measures)
+            ind.fitness = fitness
+    return population
+
+
+def survivor_selection(population: Population, config: EASettings) -> Population:
+    random.shuffle(population)
+    current_pop_size = len(population)
+    for idx in range(len(population)):
+        ind_i = population[idx]
+        ind_j = population[idx + 1]
+
+        # Kill worse individual
+        if ind_i.fitness > ind_j.fitness and config.is_maximisation:
+            ind_j.alive = False
+        else:
+            ind_i.alive = False
+
+        # Termination condition
+        current_pop_size -= 1
+        if current_pop_size <= config.target_population_size:
+            break
+    return population
+
+
+def create_individual(config: EASettings) -> Individual:
+    ind = Individual()
+    ind.genotype = config.genotype.to_json(config.genotype.create_individual())
+    return ind
+
+def read_config_file() -> EASettings:
+    cfg = tomllib.loads(Path("examples/config.toml").read_text())
+
+    # Resolve the active operators from the chosen genotype profile
+    gname = cfg["run"]["genotype"]
+    gblock = cfg["genotypes"][gname]
+    mutation_name = cfg["run"].get("mutation", gblock["defaults"]["mutation"])
+    crossover_name = cfg["run"].get("crossover", gblock["defaults"]["crossover"])
+    task = cfg["run"]["task"]
+    mutation_params = gblock.get("mutation", {}).get("params", {})
+    crossover_params = gblock.get("crossover", {}).get("params", {})
+
+    target_robot_path = cfg["task"]["evolve_to_copy"]["target_robot_path"] if task == "evolve_to_copy" else None
+
+    genotype = GENOTYPES_MAPPING[gname]
+
+    mutation = genotype.get_mutator_object()
+    mutation.set_which_mutation(mutation_name)
+    crossover = genotype.get_crossover_object()
+    crossover.set_which_crossover(crossover_name)
+
+    settings = EASettings(
+        quiet=cfg["ec"]["quiet"],
+        is_maximisation=cfg["ec"]["is_maximisation"],
+        first_generation_id=cfg["ec"]["first_generation_id"],
+        num_of_generations=cfg["ec"]["num_of_generations"],
+        target_population_size=cfg["ec"]["target_population_size"],
+        genotype=genotype,
+        mutation=mutation,
+        mutation_params=mutation_params,
+        crossover=crossover,
+        crossover_params=crossover_params,
+        task=task,
+        target_robot_file_path=Path(target_robot_path),
+        output_folder=Path(cfg["data"]["output_folder"]),
+        db_file_name=cfg["data"]["db_file_name"],
+        db_handling=cfg["data"]["db_handling"],
+        db_file_path=Path(cfg["data"]["output_folder"]) / cfg["data"]["db_file_name"],
+    )
+    return settings
+
+
+# Example usage
+def analyze_evolution_videos(analyzer, populations, decoder):
+    """Create evolution videos for different visualizations."""
+
+    analyzer.create_evolution_video(
+        populations=populations,
+        decoder=decoder,
+        plot_method_name="plot_fitness_distributions",
+        video_filename="videos/fitness_distributions.mp4"
+    )
+
+    analyzer.create_evolution_video(
+        populations=populations,
+        decoder=decoder,
+        plot_method_name="plot_pairwise_feature_landscapes",
+        video_filename="videos/pairwise_feature_landscapes.mp4"
+    )
+
+
+def main() -> None:
+    """Entry point."""
+    config = read_config_file()
+    # Create initial population
+    population_list = [create_individual(config) for _ in range(10)]
+    population_list = evaluate(population_list, config)
+
+    # Create EA steps
+
+    ops = [
+        EAStep("parent_selection", partial(parent_selection, config=config)),
+        EAStep("crossover",        partial(crossover,        config=config)),
+        EAStep("mutation",         partial(mutation,         config=config)),
+        EAStep("evaluation",       partial(evaluate,         config=config)),
+        EAStep("survivor_selection", partial(survivor_selection, config=config)),
+    ]
+
+    # Initialize EA
+    ea = EA(
+        population_list,
+        operations=ops,
+        num_of_generations=100,
+    )
+
+    ea.run()
+
+    best = ea.get_solution(only_alive=False)
+    console.log(best)
+
+    median = ea.get_solution("median", only_alive=False)
+    console.log(median)
+
+    worst = ea.get_solution("worst", only_alive=False)
+    console.log(worst)
+
+    fitnesses = []
+
+    populations = []
+    for i in range(100):
+        ea.fetch_population(only_alive=False, best_comes=None, custom_logic=[Individual.time_of_birth==i])
+        individuals = ea.population
+        populations.append(individuals)
+        avg_fitness = sum(ind.fitness for ind in individuals) / len(individuals) if individuals else 0
+        console.log(f"Generation {i}: Avg Fitness = {avg_fitness}")
+        fitnesses.append(avg_fitness)
+
+    # Line plot of the fitness
+    plt.plot(range(100), fitnesses, marker='o')
+    plt.title('Average Fitness Over Generations')
+    plt.xlabel('Generation')
+    plt.ylabel('Average Fitness')
+    plt.savefig('average_fitness_over_generations_lsystem.png')
+    plt.show()
+
+    morphology_analyzer = MorphologyAnalyzer()
+    morphology_analyzer.load_target_robots(config.target_robot_file_path)
+
+    #analyze_evolution_videos(morphology_analyzer, populations, lambda x: config.genotype.to_digraph(config.genotype.from_json(x)))
+
+    # Launch interactive dashboard
+    print("\nLaunching Evolution Dashboard...")
+
+    decoder = lambda individual: config.genotype.to_digraph(config.genotype.from_json(individual.genotype))
+    run_dashboard(populations, decoder, config)
+
+    #morphology_analyzer.load_population(individuals, lambda x: config.genotype.to_digraph(config.genotype.from_json(x))) ##???? why not juse make a method of the tree genome?
+    #morphology_analyzer.compute_fitness_scores()
+    #morphology_analyzer.plot_fitness_distributions()
+    #morphology_analyzer.plot_target_descriptors_pca()
+    #morphology_analyzer.plot_fitness_landscapes()
+    #morphology_analyzer.analyze_morphological_diversity()
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/graph.json b/examples/graph.json
new file mode 100644
index 00000000..734a7db0
--- /dev/null
+++ b/examples/graph.json
@@ -0,0 +1,94 @@
+{
+    "directed": true,
+    "multigraph": false,
+    "graph": {},
+    "nodes": [
+        {
+            "type": "CORE",
+            "rotation": "DEG_0",
+            "id": 0
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_270",
+            "id": 1
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 2
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 3
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_45",
+            "id": 4
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_45",
+            "id": 5
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_45",
+            "id": 6
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_45",
+            "id": 7
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_180",
+            "id": 8
+        }
+    ],
+    "edges": [
+        {
+            "face": "BACK",
+            "source": 0,
+            "target": 1
+        },
+        {
+            "face": "RIGHT",
+            "source": 0,
+            "target": 2
+        },
+        {
+            "face": "TOP",
+            "source": 0,
+            "target": 6
+        },
+        {
+            "face": "TOP",
+            "source": 0,
+            "target": 7
+        },
+        {
+            "face": "BOTTOM",
+            "source": 0,
+            "target": 8
+        },
+        {
+            "face": "RIGHT",
+            "source": 2,
+            "target": 3
+        },
+        {
+            "face": "TOP",
+            "source": 2,
+            "target": 4
+        },
+        {
+            "face": "TOP",
+            "source": 2,
+            "target": 5
+        }
+    ]
+}
\ No newline at end of file
diff --git a/examples/lsystem_graph.json b/examples/lsystem_graph.json
new file mode 100755
index 00000000..9fc96aea
--- /dev/null
+++ b/examples/lsystem_graph.json
@@ -0,0 +1,131 @@
+{
+    "directed": true,
+    "multigraph": false,
+    "graph": {},
+    "nodes": [
+        {
+            "type": "CORE",
+            "rotation": 0,
+            "id": "C0"
+        },
+        {
+            "type": "HINGE",
+            "rotation": 45,
+            "id": "H1"
+        },
+        {
+            "type": "BRICK",
+            "rotation": 45,
+            "id": "B2"
+        },
+        {
+            "type": "BRICK",
+            "rotation": 0,
+            "id": "B3"
+        },
+        {
+            "type": "HINGE",
+            "rotation": 45,
+            "id": "H4"
+        },
+        {
+            "type": "BRICK",
+            "rotation": 0,
+            "id": "B5"
+        },
+        {
+            "type": "HINGE",
+            "rotation": 45,
+            "id": "H6"
+        },
+        {
+            "type": "HINGE",
+            "rotation": 45,
+            "id": "H7"
+        },
+        {
+            "type": "BRICK",
+            "rotation": 45,
+            "id": "B8"
+        },
+        {
+            "type": "HINGE",
+            "rotation": 45,
+            "id": "H9"
+        },
+        {
+            "type": "BRICK",
+            "rotation": 45,
+            "id": "B10"
+        },
+        {
+            "type": "BRICK",
+            "rotation": 0,
+            "id": "B11"
+        },
+        {
+            "type": "HINGE",
+            "rotation": 45,
+            "id": "H12"
+        },
+        {
+            "type": "N",
+            "rotation": 0,
+            "id": "N13"
+        }
+    ],
+    "edges": [
+        {
+            "source": "C0",
+            "target": "H1"
+        },
+        {
+            "source": "C0",
+            "target": "B5"
+        },
+        {
+            "source": "C0",
+            "target": "H9"
+        },
+        {
+            "source": "C0",
+            "target": "N13"
+        },
+        {
+            "source": "H1",
+            "target": "B2"
+        },
+        {
+            "source": "B2",
+            "target": "B3"
+        },
+        {
+            "source": "B3",
+            "target": "H4"
+        },
+        {
+            "source": "B5",
+            "target": "H6"
+        },
+        {
+            "source": "H6",
+            "target": "H7"
+        },
+        {
+            "source": "H7",
+            "target": "B8"
+        },
+        {
+            "source": "H9",
+            "target": "B10"
+        },
+        {
+            "source": "B10",
+            "target": "B11"
+        },
+        {
+            "source": "B11",
+            "target": "H12"
+        }
+    ]
+}
\ No newline at end of file
diff --git a/examples/morphology_fitness_analysis.py b/examples/morphology_fitness_analysis.py
new file mode 100644
index 00000000..4a012451
--- /dev/null
+++ b/examples/morphology_fitness_analysis.py
@@ -0,0 +1,610 @@
+#!/usr/bin/env python3
+"""
+Morphological fitness analysis and visualization.
+
+This script:
+1. Loads target robot JSONs and computes their 6D morphological descriptors
+2. Generates random robots using tree genome
+3. Computes fitness as distance to target descriptors
+4. Visualizes fitness landscapes using PCA and various plots
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+#import seaborn as sns
+from sklearn.decomposition import PCA
+from sklearn.manifold import TSNE
+from typing import List, Tuple, Callable, Any
+import json
+from pathlib import Path
+import imageio.v2 as imageio
+
+# Import ARIEL modules
+from ariel.utils.graph_ops import load_robot_json_file
+from ariel.utils.morphological_descriptor import MorphologicalMeasures
+from ariel.ec.genotypes.tree.tree_genome import TreeGenome, TreeNode
+
+import ariel.body_phenotypes.robogen_lite.config as config
+from ariel.ec.a001 import Individual
+import networkx as nx
+import matplotlib.animation as animation
+
+type Population = List[Individual]
+
+# Set random seed for reproducibility
+np.random.seed(42)
+
+def compute_6d_descriptor(robot_graph: nx.DiGraph) -> np.ndarray:
+        """Compute 6D morphological descriptor vector."""
+
+        measures = MorphologicalMeasures(robot_graph)
+        # Handle potential division by zero or missing P for non-2D robots
+        try:
+            P = measures.P if measures.is_2d else 0.0
+        except:
+            P = 0.0
+
+        descriptor = np.array([
+            measures.B,  # Branching
+            measures.L,  # Limbs
+            measures.E,  # Extensiveness (length of limbs)
+            measures.S,  # Symmetry
+            P,           # Proportion (2D only)
+            measures.J   # Joints
+        ])
+        return descriptor
+
+
+def load_target_robot(json_path: str):
+    """Load target robots and compute their descriptors."""
+    try:
+        robot_graph = load_robot_json_file(json_path)
+        descriptor = compute_6d_descriptor(robot_graph)
+
+        # Extract name from path
+        name = Path(json_path).stem
+        print(f"Loaded {name}: {descriptor}")
+        return descriptor
+
+    except Exception as e:
+        print(f"Error loading {json_path}: {e}")
+
+
+def compute_fitness_scores(individual_descriptors, target_descriptors):
+    """Compute fitness scores as mean of distances in each dimension to each target."""
+
+    fitness_scores = []
+
+    # Compute absolute differences in each dimension
+    dimension_distances = np.abs(individual_descriptors - target_descriptors)
+    # Take mean across dimensions to get fitness score
+    mean_distances = np.mean(dimension_distances)
+    # Convert to fitness (higher is better, so use negative distance)
+    fitness = -mean_distances
+    fitness_scores.append(fitness)
+
+    return np.array(fitness_scores)
+
+
+class MorphologyAnalyzer:
+    """Analyze and visualize morphological fitness landscapes."""
+
+    def __init__(self):
+        self.target_descriptors = []
+        self.target_names = []
+        self.descriptors = []
+        self.fitness_scores = []
+
+    def compute_6d_descriptor(self, robot_graph) -> np.ndarray:
+        """Compute 6D morphological descriptor vector."""
+
+        measures = MorphologicalMeasures(robot_graph)
+        # Handle potential division by zero or missing P for non-2D robots
+        try:
+            P = measures.P if measures.is_2d else 0.0
+        except:
+            P = 0.0
+
+        descriptor = np.array([
+            measures.B,  # Branching
+            measures.L,  # Limbs
+            measures.E,  # Extensiveness (length of limbs)
+            measures.S,  # Symmetry
+            P,           # Proportion (2D only)
+            measures.J   # Joints
+        ])
+        return descriptor
+
+
+    def load_target_robots(self, *json_paths: str):
+        """Load target robots and compute their descriptors."""
+        self.target_descriptors = []
+        self.target_names = []
+
+        for json_path in json_paths:
+            try:
+                robot_graph = load_robot_json_file(json_path)
+                descriptor = self.compute_6d_descriptor(robot_graph)
+                self.target_descriptors.append(descriptor)
+
+                # Extract name from path
+                name = Path(json_path).stem
+                self.target_names.append(name)
+
+                print(f"Loaded {name}: {descriptor}")
+
+            except Exception as e:
+                print(f"Error loading {json_path}: {e}")
+
+        self.target_descriptors = np.array(self.target_descriptors)
+
+    def generate_random_robot(self, max_depth: int = 3, branch_prob: float = 0.6) -> TreeGenome:
+        """Generate a random robot using tree genome."""
+        # Create root with CORE
+        root = TreeNode(
+            module_type=config.ModuleType.CORE,
+            module_rotation=config.ModuleRotationsIdx.DEG_0
+        )
+
+        # Add random children
+        self._add_random_children(root, max_depth, branch_prob)
+
+        genome = TreeGenome(root)
+        return genome
+
+    def _add_random_children(self, node: TreeNode, max_depth: int, branch_prob: float):
+        """Recursively add random children to a node."""
+        if max_depth <= 0:
+            return
+
+        available_faces = node.available_faces()
+
+        for face in available_faces:
+            if np.random.random() < branch_prob:
+                # Choose random module type (excluding CORE and NONE)
+                module_types = [mt for mt in config.ModuleType
+                              if mt not in {config.ModuleType.CORE, config.ModuleType.NONE}]
+                module_type = np.random.choice(module_types)
+
+                # Choose random rotation
+                rotation = np.random.choice(list(config.ModuleRotationsIdx))
+
+                # Create child node
+                child = TreeNode(module_type=module_type, module_rotation=rotation)
+                node._set_face(face, child)
+
+                # Recursively add children with reduced depth
+                self._add_random_children(child, max_depth - 1, branch_prob * 0.7)
+
+    def load_population(self, population: Population, decoder: Callable[[Individual], nx.DiGraph]):
+        """Load a population of robots and compute their descriptors."""
+        self.descriptors = []
+        descriptors = []
+        for ind in population:
+            robot_graph: nx.DiGraph = decoder(ind.genotype)
+            descriptor = self.compute_6d_descriptor(robot_graph)
+            descriptors.append(descriptor)
+
+        self.descriptors = np.array(descriptors)
+
+    def generate_random_population(self, n_robots: int = 100) -> List[np.ndarray]:
+        """Generate a population of random robots and compute their descriptors."""
+        self.descriptors = []
+        print(f"Generating {n_robots} random robots...")
+        descriptors = []
+
+        for i in range(n_robots):
+            if i % 20 == 0:
+                print(f"Generated {i}/{n_robots} robots")
+
+            # Generate random robot
+            genome = self.generate_random_robot()
+
+            # Decode to graph
+            robot_graph = genome.to_digraph(genome) #! THIS DOES NOT WORK, to_digraph is a static method that needs a genome as an argument
+
+            # Compute descriptor
+            descriptor = self.compute_6d_descriptor(robot_graph)
+            descriptors.append(descriptor)
+
+
+
+        self.descriptors = np.array(descriptors)
+        return self.descriptors
+
+    def compute_fitness_scores(self):
+        """Compute fitness scores as mean of distances in each dimension to each target."""
+        if len(self.target_descriptors) == 0 or len(self.descriptors) == 0:
+            raise ValueError("Need both target and random descriptors")
+
+        self.fitness_scores = []
+
+        for target_desc in self.target_descriptors:
+            # Compute absolute differences in each dimension
+            dimension_distances = np.abs(self.descriptors - target_desc)
+            # Take mean across dimensions to get fitness score
+            mean_distances = np.mean(dimension_distances, axis=1)
+            # Convert to fitness (higher is better, so use negative distance)
+            fitness = -mean_distances
+            self.fitness_scores.append(fitness)
+
+        self.fitness_scores = np.array(self.fitness_scores)
+
+    def plot_target_descriptors_pca(self, return_fig: bool = False):
+        """Plot target robots in PCA-reduced space."""
+        if len(self.target_descriptors) == 0:
+            return
+
+        fig, axes = plt.subplots(1, 2, figsize=(15, 6))
+
+        # Combine all descriptors for PCA fitting
+        all_desc = np.vstack([self.target_descriptors, self.descriptors])
+        pca = PCA(n_components=2)
+        all_pca = pca.fit_transform(all_desc)
+
+        target_pca = all_pca[:len(self.target_descriptors)]
+        random_pca = all_pca[len(self.target_descriptors):]
+
+        # Plot 1: PCA visualization
+        axes[0].scatter(random_pca[:, 0], random_pca[:, 1],
+                       alpha=0.3, c='purple', s=20, label='Evolved robots')
+
+        colors = ['red', 'blue', 'green', 'orange']
+        for i, (target, name) in enumerate(zip(target_pca, self.target_names)):
+            color = colors[i % len(colors)]
+            axes[0].scatter(target[0], target[1],
+                           c=color, s=100, marker='*',
+                           label=f'Target: {name}', edgecolors='black')
+
+        axes[0].set_xlabel(f'PC1 ({pca.explained_variance_ratio_[0]:.2%} variance)')
+        axes[0].set_ylabel(f'PC2 ({pca.explained_variance_ratio_[1]:.2%} variance)')
+        axes[0].set_title('Morphological Space (PCA)')
+        axes[0].legend()
+        axes[0].grid(True, alpha=0.3)
+
+        # Plot 2: Feature importance
+        feature_names = ['Branching', 'Limbs', 'Extensiveness', 'Symmetry', 'Proportion', 'Joints']
+
+        pc1_importance = np.abs(pca.components_[0])
+        pc2_importance = np.abs(pca.components_[1])
+
+        x = np.arange(len(feature_names))
+        width = 0.35
+
+        axes[1].bar(x - width/2, pc1_importance, width, label='PC1', alpha=0.8)
+        axes[1].bar(x + width/2, pc2_importance, width, label='PC2', alpha=0.8)
+
+        axes[1].set_xlabel('Morphological Features')
+        axes[1].set_ylabel('Absolute Component Weight')
+        axes[1].set_title('PCA Feature Importance')
+        axes[1].set_xticks(x)
+        axes[1].set_xticklabels(feature_names, rotation=45)
+        axes[1].legend()
+        axes[1].grid(True, alpha=0.3)
+
+        plt.tight_layout()
+
+        if return_fig:
+            return fig
+        else:
+            plt.show()
+            return fig
+
+    def plot_fitness_landscapes(self, return_fig: bool = False):
+        """Plot fitness landscapes for each target."""
+        if len(self.fitness_scores) == 0:
+            self.compute_fitness_scores()
+
+        # Use PCA for dimensionality reduction
+        all_desc = np.vstack([self.target_descriptors, self.descriptors])
+        pca = PCA(n_components=2)
+        all_pca = pca.fit_transform(all_desc)
+
+        target_pca = all_pca[:len(self.target_descriptors)]
+        evolved_pca = all_pca[len(self.target_descriptors):]
+
+        n_targets = len(self.target_names)
+        fig, axes = plt.subplots(2, (n_targets + 1) // 2, figsize=(15, 10))
+        if n_targets == 1:
+            axes = [axes]
+        axes = axes.flatten() if n_targets > 1 else axes[0]
+
+        for i, (target_name, fitness) in enumerate(zip(self.target_names, self.fitness_scores)):
+            ax = axes[i]
+
+            # Create scatter plot with fitness as color
+            scatter = ax.scatter(evolved_pca[:, 0], evolved_pca[:, 1],
+                               c=fitness, cmap='viridis', alpha=0.6, s=30)
+
+            # Mark target location
+            ax.scatter(target_pca[i, 0], target_pca[i, 1],
+                      c='red', s=200, marker='*',
+                      edgecolors='black', linewidths=2,
+                      label=f'Target: {target_name}')
+
+            ax.set_xlabel(f'PC1 ({pca.explained_variance_ratio_[0]:.2%})')
+            ax.set_ylabel(f'PC2 ({pca.explained_variance_ratio_[1]:.2%})')
+            ax.set_title(f'Fitness Landscape: {target_name}')
+            ax.legend()
+            ax.grid(True, alpha=0.3)
+
+            # Add colorbar
+            plt.colorbar(scatter, ax=ax, label='Fitness')
+
+        # Hide unused subplots
+        for j in range(n_targets, len(axes)):
+            axes[j].set_visible(False)
+
+        plt.tight_layout()
+
+        if return_fig:
+            return fig
+        else:
+            plt.show()
+            return fig
+
+    def plot_fitness_distributions(self, return_fig: bool = False):
+        """Plot fitness distributions for each target."""
+        if len(self.fitness_scores) == 0:
+            self.compute_fitness_scores()
+
+        fig, axes = plt.subplots(1, 2, figsize=(15, 6))
+
+        # Compute target scores (perfect match = 0 distance = 0 fitness)
+        target_scores = [0.0] * len(self.target_names)  # Perfect match has 0 mean distance
+
+        # Plot 1: Fitness distributions
+        for i, (target_name, fitness) in enumerate(zip(self.target_names, self.fitness_scores)):
+            # Get target score info for legend
+            target_score = target_scores[i]
+            best_fitness = np.max(fitness)
+            mean_fitness = np.mean(fitness)
+
+            label = f'{target_name} (target: {target_score:.3f}, best: {best_fitness:.3f}, mean: {mean_fitness:.3f})'
+            axes[0].hist(fitness, bins=30, alpha=0.7, label=label, density=True)
+
+            # Mark target score with vertical line
+            axes[0].axvline(target_score, color=f'C{i}', linestyle='--', linewidth=2, alpha=0.8)
+
+        axes[0].set_xlabel('Fitness (negative mean distance)')
+        axes[0].set_ylabel('Density')
+        axes[0].set_title('Fitness Distributions')
+        axes[0].legend()
+        axes[0].grid(True, alpha=0.3)
+
+        # Plot 2: Best fitness per target
+        best_fitness = [np.max(fitness) for fitness in self.fitness_scores]
+        mean_fitness = [np.mean(fitness) for fitness in self.fitness_scores]
+
+        x = np.arange(len(self.target_names))
+        width = 0.35
+
+        axes[1].bar(x - width/2, best_fitness, width, label='Best', alpha=0.8)
+        axes[1].bar(x + width/2, mean_fitness, width, label='Mean', alpha=0.8)
+
+        axes[1].set_xlabel('Target Robot')
+        axes[1].set_ylabel('Fitness')
+        axes[1].set_title('Fitness Statistics')
+        axes[1].set_xticks(x)
+        axes[1].set_xticklabels(self.target_names)
+        axes[1].legend()
+        axes[1].grid(True, alpha=0.3)
+
+        plt.tight_layout()
+
+        if return_fig:
+            return fig
+        else:
+            plt.show()
+            return fig
+
+    def analyze_morphological_diversity(self, return_fig: bool = False):
+        """Analyze diversity in the random population."""
+        if len(self.descriptors) == 0:
+            return
+
+        fig, axes = plt.subplots(2, 3, figsize=(18, 12))
+        axes = axes.flatten()
+
+        feature_names = ['Branching', 'Limbs', 'Extensiveness', 'Symmetry', 'Proportion', 'Joints']
+
+        for i, feature_name in enumerate(feature_names):
+            ax = axes[i]
+
+            # Plot distribution of random robots
+            random_mean = np.mean(self.descriptors[:, i])
+            random_std = np.std(self.descriptors[:, i])
+            ax.hist(self.descriptors[:, i], bins=30, alpha=0.7,
+                   density=True, label=f'Random robots (μ={random_mean:.3f}, σ={random_std:.3f})')
+
+            # Mark target values
+            for j, target_name in enumerate(self.target_names):
+                target_value = self.target_descriptors[j, i]
+                ax.axvline(target_value, color=f'C{j+1}', linestyle='--',
+                          linewidth=2, label=f'{target_name}: {target_value:.3f}')
+
+            ax.set_xlabel(feature_name)
+            ax.set_ylabel('Density')
+            ax.set_title(f'{feature_name} Distribution')
+            ax.legend()
+            ax.grid(True, alpha=0.3)
+
+        plt.tight_layout()
+
+        if return_fig:
+            return fig
+        else:
+            plt.show()
+            return fig
+
+    def plot_pairwise_feature_landscapes(self, features: List[str] = None, return_fig: bool = False):
+        """Plot fitness landscapes for pairwise combinations of selected morphological features.
+
+        Parameters:
+        -----------
+        features : List[str], optional
+            List of feature names to include. By default includes all features:
+            ['Branching', 'Limbs', 'Extensiveness', 'Symmetry', 'Proportion', 'Joints']
+        return_fig : bool, optional
+            Whether to return the figure instead of showing it
+        """
+        if len(self.fitness_scores) == 0:
+            self.compute_fitness_scores()
+
+        # All available features with their indices
+        all_feature_names = ['Branching', 'Limbs', 'Extensiveness', 'Symmetry', 'Proportion', 'Joints']
+
+        # Use all features by default
+        if features is None:
+            features = all_feature_names.copy()
+
+        # Get indices of selected features
+        feature_indices = []
+        selected_feature_names = []
+        for feature in features:
+            if feature in all_feature_names:
+                idx = all_feature_names.index(feature)
+                feature_indices.append(idx)
+                selected_feature_names.append(feature)
+            else:
+                print(f"Warning: Feature '{feature}' not recognized. Available features: {all_feature_names}")
+
+        n_features = len(feature_indices)
+        if n_features < 2:
+            print("Error: Need at least 2 features for pairwise plots")
+            return None
+
+        # Create all pairwise combinations
+        pairs = []
+        pair_names = []
+        for i in range(n_features):
+            for j in range(i + 1, n_features):
+                pairs.append((feature_indices[i], feature_indices[j]))
+                pair_names.append((selected_feature_names[i], selected_feature_names[j]))
+
+        n_pairs = len(pairs)
+
+        # Calculate optimal subplot grid
+        if n_pairs <= 3:
+            rows, cols = 1, n_pairs
+        elif n_pairs <= 6:
+            rows, cols = 2, 3
+        elif n_pairs <= 12:
+            rows, cols = 3, 4
+        else:
+            rows, cols = 3, 5  # For 15 pairs (all features)
+
+        # Create subplot grid
+        fig, axes = plt.subplots(rows, cols, figsize=(5*cols, 5*rows))
+        if n_pairs == 1:
+            axes = [axes]
+        else:
+            axes = axes.flatten() if rows > 1 or cols > 1 else [axes]
+
+        # Use the first target for fitness coloring (could be extended for multiple targets)
+        fitness_values = self.fitness_scores[0] if len(self.fitness_scores) > 0 else np.zeros(len(self.descriptors))
+
+        for idx, ((i, j), (name_i, name_j)) in enumerate(zip(pairs, pair_names)):
+            if idx >= len(axes):
+                break
+
+            ax = axes[idx]
+
+            # Extract feature pairs
+            x_values = self.descriptors[:, i]
+            y_values = self.descriptors[:, j]
+
+            # Create scatter plot with fitness as color
+            scatter = ax.scatter(x_values, y_values, c=fitness_values,
+                               cmap='viridis', alpha=0.6, s=30)
+            # Add target locations for all targets
+            colors = ['red', 'blue', 'green', 'orange', 'purple']
+            for target_idx, (target_desc, target_name) in enumerate(zip(self.target_descriptors, self.target_names)):
+                color = colors[target_idx % len(colors)]
+                ax.scatter(target_desc[i], target_desc[j],
+                          c=color, s=200, marker='*',
+                          edgecolors='black', linewidths=2,
+                          label=f'Target: {target_name}')
+
+            ax.set_xlabel(name_i)
+            ax.set_ylabel(name_j)
+            ax.set_title(f'{name_i} vs {name_j}')
+            ax.grid(True, alpha=0.3)
+
+            # Add legend only to first subplot to avoid clutter
+            if idx == 0:
+                ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
+
+            # Add colorbar to each subplot
+            plt.colorbar(scatter, ax=ax, label='Fitness', shrink=0.8)
+
+        # Hide unused subplots
+        for idx in range(n_pairs, len(axes)):
+            axes[idx].set_visible(False)
+
+        plt.tight_layout()
+
+        if return_fig:
+            return fig
+        else:
+            plt.show()
+            return fig
+
+    def create_evolution_video(
+        self,
+        populations,
+        decoder,
+        plot_method_name: str,
+        video_filename: str = "evolution_video.mp4",
+        fps: int = 2,
+        dpi: int = 100,
+        **plot_kwargs
+    ):
+        """Create video by converting each plotted figure into an RGB array."""
+        plot_method = getattr(self, plot_method_name)
+        assert self.target_descriptors is not None, "Target descriptors not loaded"
+        frames = []
+
+        for generation_idx, current_pop in enumerate(populations):
+            # Compute current data
+            self.load_population(current_pop, decoder)
+            self.compute_fitness_scores()
+
+            # Generate figure (your existing plot method already returns fig)
+            fig = plot_method(return_fig=True, **plot_kwargs)
+            fig.suptitle(f"Generation {generation_idx}", fontsize=16)
+
+            fig.canvas.draw()
+
+            # --- Convert figure to numpy array ---
+            buf = np.asarray(fig.canvas.buffer_rgba())
+            frame = buf[..., :3].copy()  # drop alpha channel
+            frames.append(frame)
+            plt.close(fig)
+
+        # --- Write video ---
+        print("Saving {len(frames)} frames to {video_filename}...")
+        imageio.mimsave(video_filename, frames, fps=fps, quality=8)
+        print(" Saved: {video_filename}")
+
+
+def main():
+    """Main analysis function."""
+    # Define target robot paths
+    target_paths = [
+        "examples/target_robots/small_robot_8.json",
+        "examples/target_robots/medium_robot_15.json",
+        "examples/target_robots/large_robot_25.json"
+    ]
+
+    analyzer = MorphologyAnalyzer()
+    analyzer.load_target_robots(*target_paths)
+    analyzer.generate_random_population(n_robots=200)
+    analyzer.compute_fitness_scores()
+    analyzer.plot_pairwise_feature_landscapes()
+    print("\nAnalysis complete!")
+
+
+if __name__ == "__main__":
+    main()
+
+
diff --git a/examples/mygraph.json b/examples/mygraph.json
new file mode 100644
index 00000000..231abd17
--- /dev/null
+++ b/examples/mygraph.json
@@ -0,0 +1,44 @@
+{
+    "directed": true,
+    "multigraph": false,
+    "graph": {},
+    "nodes": [
+        {
+            "type": 0,
+            "rotation": 0,
+            "id": "C-0"
+        },
+        {
+            "type": 3,
+            "rotation": 90,
+            "id": "N-1"
+        },
+        {
+            "type": 3,
+            "rotation": 270,
+            "id": "N-2"
+        },
+        {
+            "type": "BRICK",
+            "rotation": 270,
+            "id": "B-3"
+        }
+    ],
+    "edges": [
+        {
+            "face": "RIGHT",
+            "source": "C-0",
+            "target": "N-1"
+        },
+        {
+            "face": "LEFT",
+            "source": "C-0",
+            "target": "N-2"
+        },
+        {
+            "face": "TOP",
+            "source": "C-0",
+            "target": "B-3"
+        }
+    ]
+}
\ No newline at end of file
diff --git a/examples/mygraph2.json b/examples/mygraph2.json
new file mode 100644
index 00000000..4e2efb39
--- /dev/null
+++ b/examples/mygraph2.json
@@ -0,0 +1,54 @@
+{
+    "directed": true,
+    "multigraph": false,
+    "graph": {},
+    "nodes": [
+        {
+            "type": 0,
+            "rotation": 0,
+            "id": "C-0"
+        },
+        {
+            "type": "HINGE",
+            "rotation": 135,
+            "id": "H-1"
+        },
+        {
+            "type": "BRICK",
+            "rotation": 270,
+            "id": "B-2"
+        },
+        {
+            "type": 3,
+            "rotation": 45,
+            "id": "N-3"
+        },
+        {
+            "type": "BRICK",
+            "rotation": 225,
+            "id": "B-4"
+        }
+    ],
+    "edges": [
+        {
+            "face": "FRONT",
+            "source": "C-0",
+            "target": "H-1"
+        },
+        {
+            "face": "RIGHT",
+            "source": "C-0",
+            "target": "B-2"
+        },
+        {
+            "face": "TOP",
+            "source": "C-0",
+            "target": "N-3"
+        },
+        {
+            "face": "BOTTOM",
+            "source": "C-0",
+            "target": "B-4"
+        }
+    ]
+}
\ No newline at end of file
diff --git a/examples/offspring1.json b/examples/offspring1.json
new file mode 100644
index 00000000..1adabd98
--- /dev/null
+++ b/examples/offspring1.json
@@ -0,0 +1,13 @@
+{
+    "directed": true,
+    "multigraph": false,
+    "graph": {},
+    "nodes": [
+        {
+            "type": "CORE",
+            "rotation": "DEG_0",
+            "id": 0
+        }
+    ],
+    "edges": []
+}
\ No newline at end of file
diff --git a/examples/offspring2.json b/examples/offspring2.json
new file mode 100644
index 00000000..d7695167
--- /dev/null
+++ b/examples/offspring2.json
@@ -0,0 +1,314 @@
+{
+    "directed": true,
+    "multigraph": false,
+    "graph": {},
+    "nodes": [
+        {
+            "type": "CORE",
+            "rotation": "DEG_0",
+            "id": 0
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_45",
+            "id": 1
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_45",
+            "id": 2
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_45",
+            "id": 3
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_45",
+            "id": 4
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_45",
+            "id": 5
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_45",
+            "id": 6
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_45",
+            "id": 7
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_180",
+            "id": 8
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_270",
+            "id": 9
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 10
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_0",
+            "id": 11
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 12
+        },
+        {
+            "type": "NONE",
+            "rotation": "DEG_270",
+            "id": 13
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_45",
+            "id": 14
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 15
+        },
+        {
+            "type": "NONE",
+            "rotation": "DEG_270",
+            "id": 16
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_45",
+            "id": 17
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 18
+        },
+        {
+            "type": "NONE",
+            "rotation": "DEG_270",
+            "id": 19
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_45",
+            "id": 20
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 21
+        },
+        {
+            "type": "NONE",
+            "rotation": "DEG_270",
+            "id": 22
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_45",
+            "id": 23
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 24
+        },
+        {
+            "type": "NONE",
+            "rotation": "DEG_270",
+            "id": 25
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_45",
+            "id": 26
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_45",
+            "id": 27
+        },
+        {
+            "type": "NONE",
+            "rotation": "DEG_270",
+            "id": 28
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_0",
+            "id": 29
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_45",
+            "id": 30
+        }
+    ],
+    "edges": [
+        {
+            "face": "FRONT",
+            "source": 0,
+            "target": 1
+        },
+        {
+            "face": "LEFT",
+            "source": 0,
+            "target": 27
+        },
+        {
+            "face": "TOP",
+            "source": 0,
+            "target": 29
+        },
+        {
+            "face": "BOTTOM",
+            "source": 0,
+            "target": 30
+        },
+        {
+            "face": "FRONT",
+            "source": 1,
+            "target": 2
+        },
+        {
+            "face": "LEFT",
+            "source": 1,
+            "target": 24
+        },
+        {
+            "face": "BOTTOM",
+            "source": 1,
+            "target": 26
+        },
+        {
+            "face": "FRONT",
+            "source": 2,
+            "target": 3
+        },
+        {
+            "face": "LEFT",
+            "source": 2,
+            "target": 21
+        },
+        {
+            "face": "BOTTOM",
+            "source": 2,
+            "target": 23
+        },
+        {
+            "face": "FRONT",
+            "source": 3,
+            "target": 4
+        },
+        {
+            "face": "LEFT",
+            "source": 3,
+            "target": 18
+        },
+        {
+            "face": "BOTTOM",
+            "source": 3,
+            "target": 20
+        },
+        {
+            "face": "FRONT",
+            "source": 4,
+            "target": 5
+        },
+        {
+            "face": "LEFT",
+            "source": 4,
+            "target": 15
+        },
+        {
+            "face": "BOTTOM",
+            "source": 4,
+            "target": 17
+        },
+        {
+            "face": "FRONT",
+            "source": 5,
+            "target": 6
+        },
+        {
+            "face": "LEFT",
+            "source": 5,
+            "target": 12
+        },
+        {
+            "face": "BOTTOM",
+            "source": 5,
+            "target": 14
+        },
+        {
+            "face": "FRONT",
+            "source": 6,
+            "target": 7
+        },
+        {
+            "face": "LEFT",
+            "source": 6,
+            "target": 10
+        },
+        {
+            "face": "TOP",
+            "source": 6,
+            "target": 11
+        },
+        {
+            "face": "FRONT",
+            "source": 7,
+            "target": 8
+        },
+        {
+            "face": "TOP",
+            "source": 7,
+            "target": 9
+        },
+        {
+            "face": "BOTTOM",
+            "source": 12,
+            "target": 13
+        },
+        {
+            "face": "BOTTOM",
+            "source": 15,
+            "target": 16
+        },
+        {
+            "face": "BOTTOM",
+            "source": 18,
+            "target": 19
+        },
+        {
+            "face": "BOTTOM",
+            "source": 21,
+            "target": 22
+        },
+        {
+            "face": "BOTTOM",
+            "source": 24,
+            "target": 25
+        },
+        {
+            "face": "BOTTOM",
+            "source": 27,
+            "target": 28
+        }
+    ]
+}
\ No newline at end of file
diff --git a/examples/offspring3.json b/examples/offspring3.json
new file mode 100644
index 00000000..f3cf286a
--- /dev/null
+++ b/examples/offspring3.json
@@ -0,0 +1,44 @@
+{
+    "directed": true,
+    "multigraph": false,
+    "graph": {},
+    "nodes": [
+        {
+            "type": "CORE",
+            "rotation": "DEG_0",
+            "id": 0
+        },
+        {
+            "type": "NONE",
+            "rotation": "DEG_45",
+            "id": 1
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_0",
+            "id": 2
+        },
+        {
+            "type": "NONE",
+            "rotation": "DEG_0",
+            "id": 3
+        }
+    ],
+    "edges": [
+        {
+            "face": "BACK",
+            "source": 0,
+            "target": 1
+        },
+        {
+            "face": "RIGHT",
+            "source": 0,
+            "target": 2
+        },
+        {
+            "face": "LEFT",
+            "source": 0,
+            "target": 3
+        }
+    ]
+}
\ No newline at end of file
diff --git a/examples/offspring4.json b/examples/offspring4.json
new file mode 100644
index 00000000..2b7dcddf
--- /dev/null
+++ b/examples/offspring4.json
@@ -0,0 +1,74 @@
+{
+    "directed": true,
+    "multigraph": false,
+    "graph": {},
+    "nodes": [
+        {
+            "type": "CORE",
+            "rotation": "DEG_0",
+            "id": 0
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_90",
+            "id": 1
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_180",
+            "id": 2
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_180",
+            "id": 3
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_0",
+            "id": 4
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_180",
+            "id": 5
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_225",
+            "id": 6
+        }
+    ],
+    "edges": [
+        {
+            "face": "FRONT",
+            "source": 0,
+            "target": 1
+        },
+        {
+            "face": "LEFT",
+            "source": 0,
+            "target": 3
+        },
+        {
+            "face": "TOP",
+            "source": 0,
+            "target": 4
+        },
+        {
+            "face": "BOTTOM",
+            "source": 0,
+            "target": 6
+        },
+        {
+            "face": "FRONT",
+            "source": 1,
+            "target": 2
+        },
+        {
+            "face": "FRONT",
+            "source": 4,
+            "target": 5
+        }
+    ]
+}
\ No newline at end of file
diff --git a/examples/parent1-mutated.json b/examples/parent1-mutated.json
new file mode 100644
index 00000000..1adabd98
--- /dev/null
+++ b/examples/parent1-mutated.json
@@ -0,0 +1,13 @@
+{
+    "directed": true,
+    "multigraph": false,
+    "graph": {},
+    "nodes": [
+        {
+            "type": "CORE",
+            "rotation": "DEG_0",
+            "id": 0
+        }
+    ],
+    "edges": []
+}
\ No newline at end of file
diff --git a/examples/parent1.json b/examples/parent1.json
new file mode 100644
index 00000000..00fc61de
--- /dev/null
+++ b/examples/parent1.json
@@ -0,0 +1,34 @@
+{
+    "directed": true,
+    "multigraph": false,
+    "graph": {},
+    "nodes": [
+        {
+            "type": "CORE",
+            "rotation": "DEG_0",
+            "id": 0
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_135",
+            "id": 1
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_135",
+            "id": 2
+        }
+    ],
+    "edges": [
+        {
+            "face": "RIGHT",
+            "source": 0,
+            "target": 1
+        },
+        {
+            "face": "TOP",
+            "source": 0,
+            "target": 2
+        }
+    ]
+}
\ No newline at end of file
diff --git a/examples/parent2-mutated.json b/examples/parent2-mutated.json
new file mode 100644
index 00000000..68dd6888
--- /dev/null
+++ b/examples/parent2-mutated.json
@@ -0,0 +1,84 @@
+{
+    "directed": true,
+    "multigraph": false,
+    "graph": {},
+    "nodes": [
+        {
+            "type": "CORE",
+            "rotation": "DEG_0",
+            "id": 0
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_180",
+            "id": 1
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_270",
+            "id": 2
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_225",
+            "id": 3
+        },
+        {
+            "type": "NONE",
+            "rotation": "DEG_225",
+            "id": 4
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_0",
+            "id": 5
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_225",
+            "id": 6
+        },
+        {
+            "type": "NONE",
+            "rotation": "DEG_0",
+            "id": 7
+        }
+    ],
+    "edges": [
+        {
+            "face": "FRONT",
+            "source": 0,
+            "target": 1
+        },
+        {
+            "face": "BACK",
+            "source": 0,
+            "target": 3
+        },
+        {
+            "face": "LEFT",
+            "source": 0,
+            "target": 4
+        },
+        {
+            "face": "TOP",
+            "source": 0,
+            "target": 5
+        },
+        {
+            "face": "BOTTOM",
+            "source": 0,
+            "target": 6
+        },
+        {
+            "face": "FRONT",
+            "source": 1,
+            "target": 2
+        },
+        {
+            "face": "LEFT",
+            "source": 6,
+            "target": 7
+        }
+    ]
+}
\ No newline at end of file
diff --git a/examples/parent2.json b/examples/parent2.json
new file mode 100644
index 00000000..a7445b90
--- /dev/null
+++ b/examples/parent2.json
@@ -0,0 +1,64 @@
+{
+    "directed": true,
+    "multigraph": false,
+    "graph": {},
+    "nodes": [
+        {
+            "type": "CORE",
+            "rotation": "DEG_0",
+            "id": 0
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_45",
+            "id": 1
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_135",
+            "id": 2
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_135",
+            "id": 3
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_45",
+            "id": 4
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_135",
+            "id": 5
+        }
+    ],
+    "edges": [
+        {
+            "face": "BACK",
+            "source": 0,
+            "target": 1
+        },
+        {
+            "face": "RIGHT",
+            "source": 0,
+            "target": 2
+        },
+        {
+            "face": "LEFT",
+            "source": 0,
+            "target": 3
+        },
+        {
+            "face": "BOTTOM",
+            "source": 0,
+            "target": 4
+        },
+        {
+            "face": "FRONT",
+            "source": 4,
+            "target": 5
+        }
+    ]
+}
\ No newline at end of file
diff --git a/examples/plotly_morphology_analysis.py b/examples/plotly_morphology_analysis.py
new file mode 100644
index 00000000..8136172d
--- /dev/null
+++ b/examples/plotly_morphology_analysis.py
@@ -0,0 +1,439 @@
+#!/usr/bin/env python3
+"""
+Plotly-based Morphological Analysis
+
+This module provides interactive morphological analysis and visualization
+using Plotly for web dashboards. It mirrors the functionality of
+MorphologyAnalyzer but returns Plotly figures instead of matplotlib plots.
+"""
+
+import numpy as np
+import plotly.graph_objects as go
+import plotly.express as px
+from plotly.subplots import make_subplots
+from sklearn.decomposition import PCA
+from sklearn.manifold import TSNE
+from typing import List, Tuple, Callable, Any
+from pathlib import Path
+import networkx as nx
+
+# Import ARIEL modules
+from ariel.utils.graph_ops import load_robot_json_file
+from ariel.utils.morphological_descriptor import MorphologicalMeasures
+from ariel.ec.genotypes.tree.tree_genome import TreeGenome, TreeNode
+import ariel.body_phenotypes.robogen_lite.config as config
+from ariel.ec.a001 import Individual
+
+type Population = List[Individual]
+
+
+class PlotlyMorphologyAnalyzer:
+    """Analyze and visualize morphological fitness landscapes using Plotly."""
+
+    def __init__(self):
+        self.target_descriptors = []
+        self.target_names = []
+        self.descriptors = []
+        self.fitness_scores = []
+
+    def compute_6d_descriptor(self, robot_graph) -> np.ndarray:
+        """Compute 6D morphological descriptor vector."""
+        measures = MorphologicalMeasures(robot_graph)
+        # Handle potential division by zero or missing P for non-2D robots
+        try:
+            P = measures.P if measures.is_2d else 0.0
+        except:
+            P = 0.0
+
+        descriptor = np.array([
+            measures.B,  # Branching
+            measures.L,  # Limbs
+            measures.E,  # Extensiveness (length of limbs)
+            measures.S,  # Symmetry
+            P,           # Proportion (2D only)
+            measures.J   # Joints
+        ])
+        return descriptor
+
+    def load_target_robots(self, *json_paths: str):
+        """Load target robots and compute their descriptors."""
+        self.target_descriptors = []
+        self.target_names = []
+
+        for json_path in json_paths:
+            try:
+                robot_graph = load_robot_json_file(json_path)
+                descriptor = self.compute_6d_descriptor(robot_graph)
+                self.target_descriptors.append(descriptor)
+
+                # Extract name from path
+                name = Path(json_path).stem
+                self.target_names.append(name)
+
+                print(f"Loaded {name}: {descriptor}")
+
+            except Exception as e:
+                print(f"Error loading {json_path}: {e}")
+
+        self.target_descriptors = np.array(self.target_descriptors)
+
+    def load_population(self, population: Population, decoder: Callable[[Individual], nx.DiGraph]):
+        """Load a population of robots and compute their descriptors."""
+        self.descriptors = []
+        descriptors = []
+        for ind in population:
+            robot_graph: nx.DiGraph = decoder(ind)
+            descriptor = self.compute_6d_descriptor(robot_graph)
+            descriptors.append(descriptor)
+
+        self.descriptors = np.array(descriptors)
+
+    def compute_fitness_scores(self):
+        """Compute fitness scores as mean of distances in each dimension to each target."""
+        if len(self.target_descriptors) == 0 or len(self.descriptors) == 0:
+            raise ValueError("Need both target and random descriptors")
+
+        self.fitness_scores = []
+
+        for target_desc in self.target_descriptors:
+            # Compute absolute differences in each dimension
+            dimension_distances = np.abs(self.descriptors - target_desc)
+            # Take mean across dimensions to get fitness score
+            mean_distances = np.mean(dimension_distances, axis=1)
+            # Convert to fitness (higher is better, so use negative distance)
+            fitness = -mean_distances
+            self.fitness_scores.append(fitness)
+
+        self.fitness_scores = np.array(self.fitness_scores)
+
+    def plot_target_descriptors_pca(self) -> go.Figure:
+        """Plot target robots in PCA-reduced space using Plotly."""
+        if len(self.target_descriptors) == 0:
+            return go.Figure()
+
+        # Combine all descriptors for PCA fitting
+        all_desc = np.vstack([self.target_descriptors, self.descriptors])
+        pca = PCA(n_components=2)
+        all_pca = pca.fit_transform(all_desc)
+
+        target_pca = all_pca[:len(self.target_descriptors)]
+        random_pca = all_pca[len(self.target_descriptors):]
+
+        fig = make_subplots(
+            rows=1, cols=2,
+            subplot_titles=['Morphological Space (PCA)', 'PCA Feature Importance']
+        )
+
+        # Plot 1: PCA visualization
+        fig.add_trace(
+            go.Scatter(
+                x=random_pca[:, 0],
+                y=random_pca[:, 1],
+                mode='markers',
+                name='Evolved robots',
+                marker=dict(color='purple', size=8, opacity=0.6)
+            ),
+            row=1, col=1
+        )
+
+        colors = ['red', 'blue', 'green', 'orange']
+        for i, (target, name) in enumerate(zip(target_pca, self.target_names)):
+            color = colors[i % len(colors)]
+            fig.add_trace(
+                go.Scatter(
+                    x=[target[0]],
+                    y=[target[1]],
+                    mode='markers',
+                    name=f'Target: {name}',
+                    marker=dict(color=color, size=15, symbol='star', line=dict(width=2, color='black'))
+                ),
+                row=1, col=1
+            )
+
+        # Plot 2: Feature importance
+        feature_names = ['Branching', 'Limbs', 'Extensiveness', 'Symmetry', 'Proportion', 'Joints']
+        pc1_importance = np.abs(pca.components_[0])
+        pc2_importance = np.abs(pca.components_[1])
+
+        fig.add_trace(
+            go.Bar(x=feature_names, y=pc1_importance, name='PC1', opacity=0.8),
+            row=1, col=2
+        )
+        fig.add_trace(
+            go.Bar(x=feature_names, y=pc2_importance, name='PC2', opacity=0.8),
+            row=1, col=2
+        )
+
+        fig.update_xaxes(title_text=f'PC1 ({pca.explained_variance_ratio_[0]:.2%} variance)', row=1, col=1)
+        fig.update_yaxes(title_text=f'PC2 ({pca.explained_variance_ratio_[1]:.2%} variance)', row=1, col=1)
+        fig.update_xaxes(title_text='Morphological Features', row=1, col=2)
+        fig.update_yaxes(title_text='Absolute Component Weight', row=1, col=2)
+
+        fig.update_layout(height=600, title_text="PCA Analysis")
+
+        return fig
+
+    def plot_fitness_landscapes(self) -> go.Figure:
+        """Plot fitness landscapes for each target using Plotly."""
+        if len(self.fitness_scores) == 0:
+            self.compute_fitness_scores()
+
+        # Use PCA for dimensionality reduction
+        all_desc = np.vstack([self.target_descriptors, self.descriptors])
+        pca = PCA(n_components=2)
+        all_pca = pca.fit_transform(all_desc)
+
+        target_pca = all_pca[:len(self.target_descriptors)]
+        evolved_pca = all_pca[len(self.target_descriptors):]
+
+        n_targets = len(self.target_names)
+        fig = make_subplots(
+            rows=1, cols=n_targets,
+            subplot_titles=[f'Fitness: {name}' for name in self.target_names]
+        )
+
+        colors = ['red', 'blue', 'green', 'orange']
+        for i, (target_name, fitness) in enumerate(zip(self.target_names, self.fitness_scores)):
+            col = i + 1
+
+            # Create scatter plot with fitness as color
+            fig.add_trace(
+                go.Scatter(
+                    x=evolved_pca[:, 0],
+                    y=evolved_pca[:, 1],
+                    mode='markers',
+                    marker=dict(
+                        color=fitness,
+                        colorscale='Viridis',
+                        size=8,
+                        opacity=0.7,
+                        colorbar=dict(title='Fitness') if i == 0 else None
+                    ),
+                    name=f'Population - {target_name}',
+                    showlegend=False
+                ),
+                row=1, col=col
+            )
+
+            # Mark target location
+            color = colors[i % len(colors)]
+            fig.add_trace(
+                go.Scatter(
+                    x=[target_pca[i, 0]],
+                    y=[target_pca[i, 1]],
+                    mode='markers',
+                    name=f'Target: {target_name}',
+                    marker=dict(color=color, size=15, symbol='star', line=dict(width=2, color='black')),
+                    showlegend=(i == 0)
+                ),
+                row=1, col=col
+            )
+
+        fig.update_layout(height=500, title_text="Fitness Landscapes")
+
+        return fig
+
+    def plot_fitness_distributions(self) -> go.Figure:
+        """Plot fitness distributions for each target using Plotly."""
+        if len(self.fitness_scores) == 0:
+            self.compute_fitness_scores()
+
+        fig = make_subplots(
+            rows=1, cols=2,
+            subplot_titles=['Fitness Distributions', 'Fitness Statistics']
+        )
+
+        # Compute target scores (perfect match = 0 distance = 0 fitness)
+        target_scores = [0.0] * len(self.target_names)
+
+        # Plot 1: Fitness distributions
+        for i, (target_name, fitness) in enumerate(zip(self.target_names, self.fitness_scores)):
+            target_score = target_scores[i]
+            best_fitness = np.max(fitness)
+            mean_fitness = np.mean(fitness)
+
+            fig.add_trace(
+                go.Histogram(
+                    x=fitness,
+                    name=f'{target_name} (target: {target_score:.3f}, best: {best_fitness:.3f}, mean: {mean_fitness:.3f})',
+                    opacity=0.7,
+                    nbinsx=30
+                ),
+                row=1, col=1
+            )
+
+            # Mark target score with vertical line
+            colors = ['red', 'blue', 'green', 'orange', 'purple']
+            fig.add_vline(
+                x=target_score,
+                line=dict(color=colors[i % len(colors)], dash='dash', width=2),
+                row=1, col=1
+            )
+
+        # Plot 2: Best fitness per target
+        best_fitness = [np.max(fitness) for fitness in self.fitness_scores]
+        mean_fitness = [np.mean(fitness) for fitness in self.fitness_scores]
+
+        fig.add_trace(
+            go.Bar(x=self.target_names, y=best_fitness, name='Best', opacity=0.8),
+            row=1, col=2
+        )
+        fig.add_trace(
+            go.Bar(x=self.target_names, y=mean_fitness, name='Mean', opacity=0.8),
+            row=1, col=2
+        )
+
+        fig.update_xaxes(title_text='Fitness (negative mean distance)', row=1, col=1)
+        fig.update_yaxes(title_text='Count', row=1, col=1)
+        fig.update_xaxes(title_text='Target Robot', row=1, col=2)
+        fig.update_yaxes(title_text='Fitness', row=1, col=2)
+
+        fig.update_layout(height=500, title_text="Fitness Distributions")
+
+        return fig
+
+    def analyze_morphological_diversity(self) -> go.Figure:
+        """Analyze diversity in the population using Plotly."""
+        if len(self.descriptors) == 0:
+            return go.Figure()
+
+        feature_names = ['Branching', 'Limbs', 'Extensiveness', 'Symmetry', 'Proportion', 'Joints']
+
+        fig = make_subplots(
+            rows=2, cols=3,
+            subplot_titles=feature_names
+        )
+
+        for i, feature_name in enumerate(feature_names):
+            row = (i // 3) + 1
+            col = (i % 3) + 1
+
+            # Plot distribution of evolved robots
+            feature_data = self.descriptors[:, i]
+            random_mean = np.mean(feature_data)
+            random_std = np.std(feature_data)
+
+            fig.add_trace(
+                go.Histogram(
+                    x=feature_data,
+                    name=f'Evolved robots (μ={random_mean:.3f}, σ={random_std:.3f})',
+                    opacity=0.7,
+                    nbinsx=30,
+                    showlegend=(i == 0)
+                ),
+                row=row, col=col
+            )
+
+            # Mark target values
+            colors = ['red', 'blue', 'green', 'orange']
+            for j, target_name in enumerate(self.target_names):
+                target_value = self.target_descriptors[j, i]
+                fig.add_vline(
+                    x=target_value,
+                    line=dict(color=colors[j % len(colors)], dash='dash', width=2),
+                    row=row, col=col
+                )
+
+        fig.update_layout(height=600, title_text="Morphological Diversity Analysis")
+
+        return fig
+
+    def plot_pairwise_feature_landscapes(self, features: List[str] = None) -> go.Figure:
+        """Plot fitness landscapes for pairwise combinations of selected morphological features using Plotly.
+
+        Parameters:
+        -----------
+        features : List[str], optional
+            List of feature names to include. By default includes all features:
+            ['Branching', 'Limbs', 'Extensiveness', 'Symmetry', 'Proportion', 'Joints']
+        """
+        if len(self.fitness_scores) == 0:
+            self.compute_fitness_scores()
+
+        # All available features with their indices
+        all_feature_names = ['Branching', 'Limbs', 'Extensiveness', 'Symmetry', 'Proportion', 'Joints']
+
+        # Use all features by default
+        if features is None:
+            features = all_feature_names.copy()
+
+        # Get indices of selected features
+        feature_indices = []
+        selected_feature_names = []
+        for feature in features:
+            if feature in all_feature_names:
+                idx = all_feature_names.index(feature)
+                feature_indices.append(idx)
+                selected_feature_names.append(feature)
+            else:
+                print(f"Warning: Feature '{feature}' not recognized. Available features: {all_feature_names}")
+
+        n_features = len(feature_indices)
+        if n_features < 2:
+            print("Error: Need at least 2 features for pairwise plots")
+            return go.Figure()
+
+        # Create all pairwise combinations - limit to 6 key pairs for display
+        key_pairs = [(0, 1), (0, 2), (1, 2), (2, 3), (3, 4), (4, 5)]
+        key_pairs = [(feature_indices[i], feature_indices[j]) for i, j in key_pairs 
+                     if i < len(feature_indices) and j < len(feature_indices)][:6]
+        
+        pair_names = [(all_feature_names[i], all_feature_names[j]) for i, j in key_pairs]
+
+        fig = make_subplots(
+            rows=2, cols=3,
+            subplot_titles=[f'{name_i} vs {name_j}' for name_i, name_j in pair_names]
+        )
+
+        # Use the first target for fitness coloring
+        fitness_values = self.fitness_scores[0] if len(self.fitness_scores) > 0 else np.zeros(len(self.descriptors))
+
+        for idx, ((i, j), (name_i, name_j)) in enumerate(zip(key_pairs, pair_names)):
+            row = (idx // 3) + 1
+            col = (idx % 3) + 1
+
+            # Extract feature pairs
+            x_values = self.descriptors[:, i]
+            y_values = self.descriptors[:, j]
+
+            # Create scatter plot with fitness as color
+            fig.add_trace(
+                go.Scatter(
+                    x=x_values,
+                    y=y_values,
+                    mode='markers',
+                    marker=dict(
+                        color=fitness_values,
+                        colorscale='Viridis',
+                        size=6,
+                        opacity=0.7,
+                        colorbar=dict(title='Fitness') if idx == 0 else None
+                    ),
+                    name='Population',
+                    showlegend=(idx == 0)
+                ),
+                row=row, col=col
+            )
+
+            # Add target locations for all targets
+            colors = ['red', 'blue', 'green', 'orange', 'purple']
+            for target_idx, (target_desc, target_name) in enumerate(zip(self.target_descriptors, self.target_names)):
+                color = colors[target_idx % len(colors)]
+                fig.add_trace(
+                    go.Scatter(
+                        x=[target_desc[i]],
+                        y=[target_desc[j]],
+                        mode='markers',
+                        name=f'Target: {target_name}' if idx == 0 else None,
+                        marker=dict(color=color, size=12, symbol='star', line=dict(width=2, color='black')),
+                        showlegend=(idx == 0)
+                    ),
+                    row=row, col=col
+                )
+
+            fig.update_xaxes(title_text=name_i, row=row, col=col)
+            fig.update_yaxes(title_text=name_j, row=row, col=col)
+
+        fig.update_layout(height=600, title_text="Pairwise Feature Landscapes")
+
+        return fig
\ No newline at end of file
diff --git a/examples/target_robots/large_robot_25.json b/examples/target_robots/large_robot_25.json
new file mode 100644
index 00000000..6bd9b51a
--- /dev/null
+++ b/examples/target_robots/large_robot_25.json
@@ -0,0 +1,254 @@
+{
+    "directed": true,
+    "multigraph": false,
+    "graph": {},
+    "nodes": [
+        {
+            "type": "CORE",
+            "rotation": "DEG_0",
+            "id": 0
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 1
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 2
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 3
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 4
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 5
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_90",
+            "id": 6
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_90",
+            "id": 7
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_90",
+            "id": 8
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_90",
+            "id": 9
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_90",
+            "id": 10
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_90",
+            "id": 11
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 12
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 13
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 14
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 15
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 16
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 17
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_45",
+            "id": 18
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_45",
+            "id": 19
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_90",
+            "id": 20
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_90",
+            "id": 21
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_135",
+            "id": 22
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_135",
+            "id": 23
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_180",
+            "id": 24
+        }
+    ],
+    "edges": [
+        {
+            "face": "FRONT",
+            "source": 0,
+            "target": 1
+        },
+        {
+            "face": "BACK",
+            "source": 0,
+            "target": 2
+        },
+        {
+            "face": "LEFT",
+            "source": 0,
+            "target": 3
+        },
+        {
+            "face": "RIGHT",
+            "source": 0,
+            "target": 4
+        },
+        {
+            "face": "TOP",
+            "source": 0,
+            "target": 5
+        },
+        {
+            "face": "BOTTOM",
+            "source": 0,
+            "target": 6
+        },
+        {
+            "face": "FRONT",
+            "source": 1,
+            "target": 7
+        },
+        {
+            "face": "RIGHT",
+            "source": 1,
+            "target": 8
+        },
+        {
+            "face": "FRONT",
+            "source": 2,
+            "target": 9
+        },
+        {
+            "face": "LEFT",
+            "source": 2,
+            "target": 10
+        },
+        {
+            "face": "FRONT",
+            "source": 3,
+            "target": 11
+        },
+        {
+            "face": "FRONT",
+            "source": 6,
+            "target": 12
+        },
+        {
+            "face": "FRONT",
+            "source": 7,
+            "target": 13
+        },
+        {
+            "face": "FRONT",
+            "source": 8,
+            "target": 14
+        },
+        {
+            "face": "FRONT",
+            "source": 9,
+            "target": 15
+        },
+        {
+            "face": "FRONT",
+            "source": 10,
+            "target": 16
+        },
+        {
+            "face": "FRONT",
+            "source": 11,
+            "target": 17
+        },
+        {
+            "face": "RIGHT",
+            "source": 4,
+            "target": 18
+        },
+        {
+            "face": "LEFT",
+            "source": 4,
+            "target": 19
+        },
+        {
+            "face": "TOP",
+            "source": 5,
+            "target": 20
+        },
+        {
+            "face": "RIGHT",
+            "source": 5,
+            "target": 21
+        },
+        {
+            "face": "LEFT",
+            "source": 12,
+            "target": 22
+        },
+        {
+            "face": "RIGHT",
+            "source": 13,
+            "target": 23
+        },
+        {
+            "face": "TOP",
+            "source": 14,
+            "target": 24
+        }
+    ]
+}
\ No newline at end of file
diff --git a/examples/target_robots/medium_robot_15.json b/examples/target_robots/medium_robot_15.json
new file mode 100644
index 00000000..7ab93174
--- /dev/null
+++ b/examples/target_robots/medium_robot_15.json
@@ -0,0 +1,154 @@
+{
+    "directed": true,
+    "multigraph": false,
+    "graph": {},
+    "nodes": [
+        {
+            "type": "CORE",
+            "rotation": "DEG_0",
+            "id": 0
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 1
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 2
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 3
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 4
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_90",
+            "id": 5
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_90",
+            "id": 6
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_90",
+            "id": 7
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_90",
+            "id": 8
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 9
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 10
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 11
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 12
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_45",
+            "id": 13
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_45",
+            "id": 14
+        }
+    ],
+    "edges": [
+        {
+            "face": "FRONT",
+            "source": 0,
+            "target": 1
+        },
+        {
+            "face": "BACK",
+            "source": 0,
+            "target": 2
+        },
+        {
+            "face": "LEFT",
+            "source": 0,
+            "target": 3
+        },
+        {
+            "face": "RIGHT",
+            "source": 0,
+            "target": 4
+        },
+        {
+            "face": "TOP",
+            "source": 0,
+            "target": 5
+        },
+        {
+            "face": "FRONT",
+            "source": 1,
+            "target": 6
+        },
+        {
+            "face": "FRONT",
+            "source": 2,
+            "target": 7
+        },
+        {
+            "face": "FRONT",
+            "source": 3,
+            "target": 8
+        },
+        {
+            "face": "FRONT",
+            "source": 5,
+            "target": 9
+        },
+        {
+            "face": "FRONT",
+            "source": 6,
+            "target": 10
+        },
+        {
+            "face": "FRONT",
+            "source": 7,
+            "target": 11
+        },
+        {
+            "face": "FRONT",
+            "source": 8,
+            "target": 12
+        },
+        {
+            "face": "RIGHT",
+            "source": 4,
+            "target": 13
+        },
+        {
+            "face": "LEFT",
+            "source": 9,
+            "target": 14
+        }
+    ]
+}
\ No newline at end of file
diff --git a/examples/target_robots/small_robot_8.json b/examples/target_robots/small_robot_8.json
new file mode 100644
index 00000000..2ac44009
--- /dev/null
+++ b/examples/target_robots/small_robot_8.json
@@ -0,0 +1,84 @@
+{
+    "directed": true,
+    "multigraph": false,
+    "graph": {},
+    "nodes": [
+        {
+            "type": "CORE",
+            "rotation": "DEG_0",
+            "id": 0
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 1
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 2
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_90",
+            "id": 3
+        },
+        {
+            "type": "HINGE",
+            "rotation": "DEG_90",
+            "id": 4
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 5
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 6
+        },
+        {
+            "type": "BRICK",
+            "rotation": "DEG_0",
+            "id": 7
+        }
+    ],
+    "edges": [
+        {
+            "face": "FRONT",
+            "source": 0,
+            "target": 1
+        },
+        {
+            "face": "BACK",
+            "source": 0,
+            "target": 2
+        },
+        {
+            "face": "LEFT",
+            "source": 0,
+            "target": 3
+        },
+        {
+            "face": "RIGHT",
+            "source": 0,
+            "target": 4
+        },
+        {
+            "face": "FRONT",
+            "source": 3,
+            "target": 5
+        },
+        {
+            "face": "FRONT",
+            "source": 4,
+            "target": 6
+        },
+        {
+            "face": "FRONT",
+            "source": 1,
+            "target": 7
+        }
+    ]
+}
\ No newline at end of file
diff --git a/examples/tree_genome_vis.py b/examples/tree_genome_vis.py
new file mode 100644
index 00000000..73a8c944
--- /dev/null
+++ b/examples/tree_genome_vis.py
@@ -0,0 +1,506 @@
+"""Assignment 3 template code."""
+
+# Standard library
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Literal
+
+import matplotlib.pyplot as plt
+import mujoco as mj
+import numpy as np
+import numpy.typing as npt
+from mujoco import viewer
+
+# Local libraries
+from ariel import console
+from ariel.body_phenotypes.robogen_lite.constructor import (
+    construct_mjspec_from_graph,
+)
+from ariel.body_phenotypes.robogen_lite.decoders.hi_prob_decoding import (
+    HighProbabilityDecoder,
+    save_graph_as_json,
+)
+from ariel.ec.genotypes.nde import NeuralDevelopmentalEncoding
+from ariel.simulation.controllers.controller import Controller
+from ariel.simulation.environments import OlympicArena
+from ariel.utils.renderers import single_frame_renderer, video_renderer
+from ariel.utils.runners import simple_runner
+from ariel.utils.tracker import Tracker
+from ariel.utils.video_recorder import VideoRecorder
+from ariel import console
+from ariel.body_phenotypes.robogen_lite.constructor import construct_mjspec_from_graph
+from ariel.ec.genotypes.tree.tree_genome import TreeGenome, TreeNode
+from ariel.body_phenotypes.robogen_lite import config
+from ariel.simulation.controllers.controller import Controller
+from ariel.simulation.environments import OlympicArena
+from ariel.utils.renderers import single_frame_renderer, video_renderer
+from ariel.utils.runners import simple_runner
+from ariel.utils.tracker import Tracker
+from ariel.utils.video_recorder import VideoRecorder
+from ariel.utils.morphological_descriptor import MorphologicalMeasures
+from ariel.utils.graph_ops import robot_json_to_digraph, load_robot_json_file
+
+
+# Type Checking
+if TYPE_CHECKING:
+    from networkx import DiGraph
+
+# Type Aliases
+type ViewerTypes = Literal["launcher", "video", "simple", "no_control", "frame"]
+
+# --- RANDOM GENERATOR SETUP --- #
+SEED = 42
+RNG = np.random.default_rng(SEED)
+
+# --- DATA SETUP ---
+SCRIPT_NAME = __file__.split("/")[-1][:-3]
+CWD = Path.cwd()
+DATA = CWD / "__data__" / SCRIPT_NAME
+DATA.mkdir(exist_ok=True)
+
+# Global variables
+SPAWN_POS = [-0.8, 0, 0.1]
+NUM_OF_MODULES = 30
+TARGET_POSITION = [5, 0, 0.5]
+
+
+def fitness_function(history: list[float]) -> float:
+    xt, yt, zt = TARGET_POSITION
+    xc, yc, zc = history[-1]
+
+    # Minimize the distance --> maximize the negative distance
+    cartesian_distance = np.sqrt(
+        (xt - xc) ** 2 + (yt - yc) ** 2 + (zt - zc) ** 2,
+    )
+    return -cartesian_distance
+
+
+def show_xpos_history(history: list[float]) -> None:
+    # Create a tracking camera
+    camera = mj.MjvCamera()
+    camera.type = mj.mjtCamera.mjCAMERA_FREE
+    camera.lookat = [2.5, 0, 0]
+    camera.distance = 10
+    camera.azimuth = 0
+    camera.elevation = -90
+
+    # Initialize world to get the background
+    mj.set_mjcb_control(None)
+    world = OlympicArena()
+    model = world.spec.compile()
+    data = mj.MjData(model)
+    save_path = str(DATA / "background.png")
+    single_frame_renderer(
+        model,
+        data,
+        camera=camera,
+        save_path=save_path,
+        save=True,
+    )
+
+    # Setup background image
+    img = plt.imread(save_path)
+    _, ax = plt.subplots()
+    ax.imshow(img)
+    w, h, _ = img.shape
+
+    # Convert list of [x,y,z] positions to numpy array
+    pos_data = np.array(history)
+
+    # Calculate initial position
+    x0, y0 = int(h * 0.483), int(w * 0.815)
+    xc, yc = int(h * 0.483), int(w * 0.9205)
+    ym0, ymc = 0, SPAWN_POS[0]
+
+    # Convert position data to pixel coordinates
+    pixel_to_dist = -((ymc - ym0) / (yc - y0))
+    pos_data_pixel = [[xc, yc]]
+    for i in range(len(pos_data) - 1):
+        xi, yi, _ = pos_data[i]
+        xj, yj, _ = pos_data[i + 1]
+        xd, yd = (xj - xi) / pixel_to_dist, (yj - yi) / pixel_to_dist
+        xn, yn = pos_data_pixel[i]
+        pos_data_pixel.append([xn + int(xd), yn + int(yd)])
+    pos_data_pixel = np.array(pos_data_pixel)
+
+    # Plot x,y trajectory
+    ax.plot(x0, y0, "kx", label="[0, 0, 0]")
+    ax.plot(xc, yc, "go", label="Start")
+    ax.plot(pos_data_pixel[:, 0], pos_data_pixel[:, 1], "b-", label="Path")
+    ax.plot(pos_data_pixel[-1, 0], pos_data_pixel[-1, 1], "ro", label="End")
+
+    # Add labels and title
+    ax.set_xlabel("X Position")
+    ax.set_ylabel("Y Position")
+    ax.legend()
+
+    # Title
+    plt.title("Robot Path in XY Plane")
+
+    # Show results
+    plt.show()
+
+def create_custom_genome() -> TreeGenome:
+    """Create your custom tree genome here.
+
+    Modify this function to define the robot structure you want to visualize.
+    You can use the TreeGenerator methods or manually build the tree.
+    """
+    # Option 1: Use predefined generators
+    # return TreeGenerator.star_shape(num_arms=4)
+    # return TreeGenerator.binary_tree(depth=3)
+    # return TreeGenerator.random_tree(max_depth=3, branching_prob=0.6)
+
+    return TreeGenerator.random_tree(max_depth=10)
+
+    #return TreeGenome.default_init()
+    # Option 2: Build manually
+    genome = TreeGenome.default_init()  # Starts with CORE module
+
+    # Add a brick to the front
+    front_brick = TreeNode(
+        config.ModuleInstance(
+            type=config.ModuleType.BRICK,
+            rotation=config.ModuleRotationsIdx.DEG_0,
+            links={}
+        )
+    )
+    genome.root.front = front_brick
+
+    # Add a hinge to the right of the core
+    right_hinge = TreeNode(
+        config.ModuleInstance(
+            type=config.ModuleType.HINGE,
+            rotation=config.ModuleRotationsIdx.DEG_90,
+            links={}
+        )
+    )
+    genome.root.right = right_hinge
+
+    # Add another brick to the front of the right hinge
+    hinge_front_brick = TreeNode(
+        config.ModuleInstance(
+            type=config.ModuleType.BRICK,
+            rotation=config.ModuleRotationsIdx.DEG_0,
+            links={}
+        )
+    )
+    right_hinge.front = hinge_front_brick
+
+    return genome
+
+def create_multi_limb_robot():
+    # Build a complex robot manually
+    genome = TreeGenome.default_init()  # Core
+
+    # Add main branches from core
+    for face in [config.ModuleFaces.FRONT, config.ModuleFaces.BACK,
+                 config.ModuleFaces.LEFT, config.ModuleFaces.RIGHT]:
+        # Add brick to each main direction
+        main_brick = TreeNode(config.ModuleInstance(
+            type=config.ModuleType.BRICK,
+            rotation=config.ModuleRotationsIdx.DEG_0,
+            links={}
+        ))
+        setattr(genome.root, face.name.lower(), main_brick)
+
+        # Add sub-branches from each main brick
+        for sub_face in [config.ModuleFaces.FRONT, config.ModuleFaces.LEFT, config.ModuleFaces.RIGHT]:
+            if sub_face in config.ALLOWED_FACES[config.ModuleType.BRICK]:
+                # Add hinge for articulation
+                hinge = TreeNode(config.ModuleInstance(
+                    type=config.ModuleType.HINGE,
+                    rotation=config.ModuleRotationsIdx.DEG_0,
+                    links={}
+                ))
+                try:
+                    setattr(main_brick, sub_face.name.lower(), hinge)
+
+                    # Add end effector brick
+                    end_brick = TreeNode(config.ModuleInstance(
+                        type=config.ModuleType.BRICK,
+                        rotation=config.ModuleRotationsIdx.DEG_0,
+                        links={}
+                    ))
+                    hinge.front = end_brick
+                except ValueError:
+                    # Face already occupied, skip
+                    pass
+    return genome
+
+def create_max_limb_robot():
+    print("\n" + "=" * 50)
+    print("Testing MAXIMUM LIMBS robot:")
+
+    # Build a robot that maximizes the number of limbs
+    genome = TreeGenome.default_init()  # Core
+
+    # Core has 6 faces: FRONT, BACK, LEFT, RIGHT, TOP, BOTTOM
+    # Each can have a brick with 5 faces: FRONT, LEFT, RIGHT, TOP, BOTTOM
+    # Each brick face can have a hinge with 1 face: FRONT
+    # Each hinge can have a final brick (limb endpoint)
+
+    core_faces = [config.ModuleFaces.FRONT, config.ModuleFaces.BACK,
+                  config.ModuleFaces.LEFT, config.ModuleFaces.RIGHT,
+                  config.ModuleFaces.TOP, config.ModuleFaces.BOTTOM]
+
+    limb_count = 0
+
+    for core_face in core_faces:
+        # Add brick to each core face
+        main_brick = TreeNode(config.ModuleInstance(
+            type=config.ModuleType.BRICK,
+            rotation=config.ModuleRotationsIdx.DEG_0,
+            links={}
+        ))
+        setattr(genome.root, core_face.name.lower(), main_brick)
+
+        # Each brick can have limbs on all its available faces
+        brick_faces = config.ALLOWED_FACES[config.ModuleType.BRICK]
+
+        for brick_face in brick_faces:
+            # Add hinge for articulation (limb joint)
+            hinge = TreeNode(config.ModuleInstance(
+                type=config.ModuleType.HINGE,
+                rotation=config.ModuleRotationsIdx.DEG_0,
+                links={}
+            ))
+
+            try:
+                setattr(main_brick, brick_face.name.lower(), hinge)
+
+                # Add end effector brick (limb endpoint)
+                end_brick = TreeNode(config.ModuleInstance(
+                    type=config.ModuleType.BRICK,
+                    rotation=config.ModuleRotationsIdx.DEG_0,
+                    links={}
+                ))
+                hinge.front = end_brick  # Hinge only has FRONT face
+                limb_count += 1
+
+            except ValueError:
+                # Face already occupied, skip
+                pass
+
+    return genome
+
+def nn_controller(
+    model: mj.MjModel,
+    data: mj.MjData,
+) -> npt.NDArray[np.float64]:
+    # Simple 3-layer neural network
+    input_size = len(data.qpos)
+    hidden_size = 8
+    output_size = model.nu
+
+    # Initialize the networks weights randomly
+    # Normally, you would use the genes of an individual as the weights,
+    # Here we set them randomly for simplicity.
+    w1 = RNG.normal(loc=0.0138, scale=0.5, size=(input_size, hidden_size))
+    w2 = RNG.normal(loc=0.0138, scale=0.5, size=(hidden_size, hidden_size))
+    w3 = RNG.normal(loc=0.0138, scale=0.5, size=(hidden_size, output_size))
+
+    # Get inputs, in this case the positions of the actuator motors (hinges)
+    inputs = data.qpos
+
+    # Run the inputs through the lays of the network.
+    layer1 = np.tanh(np.dot(inputs, w1))
+    layer2 = np.tanh(np.dot(layer1, w2))
+    outputs = np.tanh(np.dot(layer2, w3))
+
+    # Scale the outputs
+    return outputs * np.pi
+
+
+def morph_descriptors(robot_graph: Any):
+    # Analyze morphology using the phenotype graph
+    measures = MorphologicalMeasures(robot_graph)
+
+    print(f"\nMorphological measures:")
+    print(f"  Number of modules: {measures.num_modules}")
+    print(f"  Number of bricks: {measures.num_bricks}")
+    print(f"  Number of active hinges: {measures.num_active_hinges}")
+    print(f"  Bounding box: {measures.bounding_box_depth}x{measures.bounding_box_width}x{measures.bounding_box_height}")
+    print(f"  Coverage: {measures.coverage:.3f}")
+    print(f"  Branching: {measures.branching:.3f}")
+    print(f"  Limbs: {measures.limbs:.3f}")
+    print(f"  Length of limbs: {measures.length_of_limbs:.3f}")
+    print(f"  Symmetry: {measures.symmetry:.3f}")
+    print(f"  Joints: {measures.J: 3f}")
+    print(f"  Is 2D: {measures.is_2d}")
+    print(f" Size: {measures.size:.3f}")
+
+    return np.array([measures.B, measures.L, measures.E, measures.S, measures.P, measures.J])
+
+
+def simple_controller(model: mj.MjModel, data: mj.MjData) -> np.ndarray:
+    """Simple oscillating controller for robot movement."""
+    time = data.time
+    frequency = 2.0
+    amplitude = 0.8
+
+    controls = np.zeros(model.nu)
+    for i in range(model.nu):
+        phase_offset = i * np.pi / 4  # Different phase for each joint
+        controls[i] = amplitude * np.sin(frequency * time + phase_offset)
+
+    return controls
+
+
+def experiment(
+    robot: Any,
+    controller: Controller,
+    duration: int = 15,
+    mode: ViewerTypes = "viewer",
+    camera_view: str = "default"  # Add camera parameter
+) -> None:
+    """Run the simulation with random movements."""
+    # ==================================================================== #
+    # Initialise controller to controller to None, always in the beginning.
+    mj.set_mjcb_control(None)  # DO NOT REMOVE
+
+    # Initialise world
+    # Import environments from ariel.simulation.environments
+    world = OlympicArena()
+
+    # Spawn robot in the world
+    # Check docstring for spawn conditions
+    world.spawn(robot.spec, spawn_position=SPAWN_POS)
+
+    # Generate the model and data
+    # These are standard parts of the simulation USE THEM AS IS, DO NOT CHANGE
+    model = world.spec.compile()
+    data = mj.MjData(model)
+
+    # Reset state and time of simulation
+    mj.mj_resetData(model, data)
+
+    # Pass the model and data to the tracker
+    if controller.tracker is not None:
+        controller.tracker.setup(world.spec, data)
+
+    # Set the control callback function
+    # This is called every time step to get the next action.
+    args: list[Any] = []  # IF YOU NEED MORE ARGUMENTS ADD THEM HERE!
+    kwargs: dict[Any, Any] = {}  # IF YOU NEED MORE ARGUMENTS ADD THEM HERE!
+
+    mj.set_mjcb_control(
+        lambda m, d: controller.set_control(m, d, *args, **kwargs),
+    )
+
+    # ------------------------------------------------------------------ #
+    match mode:
+        case "simple":
+            # This disables visualisation (fastest option)
+            simple_runner(
+                model,
+                data,
+                duration=duration,
+            )
+        case "frame":
+            # Create custom camera based on view type
+            camera = mj.MjvCamera()
+            camera.type = mj.mjtCamera.mjCAMERA_FREE
+
+            if camera_view == "front":
+                camera.lookat = [0, 0, 0.5]
+                camera.distance = 3.0
+                camera.azimuth = 0
+                camera.elevation = -20
+            elif camera_view == "side":
+                camera.lookat = [0, 0, 0.5]
+                camera.distance = 3.0
+                camera.azimuth = 90
+                camera.elevation = -20
+            elif camera_view == "isometric":
+                camera.lookat = [0, 0, 0.5]
+                camera.distance = 4.0
+                camera.azimuth = 45
+                camera.elevation = -30
+            else:  # default
+                camera = None
+
+            save_path = str(DATA / f"robot_{camera_view}.png")
+            single_frame_renderer(
+                model,
+                data,
+                save=True,
+                save_path=save_path,
+                camera=camera
+            )
+        case "video":
+            # This records a video of the simulation
+            path_to_video_folder = str(DATA / "videos")
+            video_recorder = VideoRecorder(output_folder=path_to_video_folder)
+
+            # Render with video recorder
+            video_renderer(
+                model,
+                data,
+                duration=duration,
+                video_recorder=video_recorder,
+            )
+        case "launcher":
+            # This opens a liver viewer of the simulation
+            viewer.launch(
+                model=model,
+                data=data,
+            )
+        case "no_control":
+            # If mj.set_mjcb_control(None), you can control the limbs manually.
+            mj.set_mjcb_control(None)
+            viewer.launch(
+                model=model,
+                data=data,
+            )
+    # ==================================================================== #
+
+
+def main() -> None:
+    """Entry point."""
+    # ? ------------------------------------------------------------------ #
+
+    tree_genome = create_max_limb_robot()
+
+    robot_graph = tree_genome.to_digraph(tree_genome)
+    robot_graph = load_robot_json_file("examples/target_robots/large_robot_25.json")
+    morph_descriptors(robot_graph)
+
+    # ? ------------------------------------------------------------------ #
+    # Save the graph to a file
+    #save_graph_as_json(
+    #    robot_graph,
+    #    DATA / "robot_graph.json",
+    #)
+
+    # ? ------------------------------------------------------------------ #
+    # Print all nodes
+    core = construct_mjspec_from_graph(robot_graph)
+
+    # ? ------------------------------------------------------------------ #
+    mujoco_type_to_find = mj.mjtObj.mjOBJ_GEOM
+    name_to_bind = "core"
+    tracker = Tracker(
+        mujoco_obj_to_find=mujoco_type_to_find,
+        name_to_bind=name_to_bind,
+    )
+
+    # ? ------------------------------------------------------------------ #
+    # Simulate the robot
+    ctrl = Controller(
+        controller_callback_function=simple_controller,
+        # controller_callback_function=random_move,
+        tracker=tracker,
+    )
+
+    experiment(robot=core, controller=ctrl, mode="frame",
+               camera_view="isometric")
+
+    show_xpos_history(tracker.history["xpos"][0])
+
+    fitness = fitness_function(tracker.history["xpos"][0])
+    msg = f"Fitness of generated robot: {fitness}"
+    console.log(msg)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/generic_ea_settings.py b/generic_ea_settings.py
new file mode 100644
index 00000000..62eaf9a9
--- /dev/null
+++ b/generic_ea_settings.py
@@ -0,0 +1,134 @@
+"""
+Generic EA Settings configuration using TypeVars for genome types and operators.
+"""
+
+from typing import TypeVar, Generic, Callable, Any
+from pathlib import Path
+from pydantic import BaseSettings
+
+# Define type variables
+TGenome = TypeVar('TGenome')  # Generic genome type
+TMutation = TypeVar('TMutation', bound=Callable[[TGenome], TGenome])  # Mutation callable
+TCrossover = TypeVar('TCrossover', bound=Callable[[TGenome, TGenome], tuple[TGenome, TGenome]])  # Crossover callable
+
+# Database handling modes
+DB_HANDLING_MODES = ["delete", "append", "error"]
+
+
+class EASettings(BaseSettings, Generic[TGenome]):
+    """
+    Generic EA Settings class that can work with any genome type.
+    
+    TGenome: The genome type (e.g., TreeGenome, ListGenome, etc.)
+    """
+    quiet: bool = False
+
+    # EC mechanisms
+    is_maximisation: bool = True
+    first_generation_id: int = 0
+    num_of_generations: int = 100
+    target_population_size: int = 100
+    
+    # Generic operators - these should be callables that work with TGenome
+    mutation_operator: Callable[[TGenome], TGenome]
+    crossover_operator: Callable[[TGenome, TGenome], tuple[TGenome, TGenome]]
+    
+    # Individual creation function
+    create_individual: Callable[[], TGenome]
+
+    # Task configuration
+    task: str = "evolve_to_copy"
+    target_robot_file_path: Path | None = Path("examples/target_robots/small_robot_8.json")
+
+    # Data config
+    output_folder: Path = Path.cwd() / "__data__"
+    db_file_name: str = "database.db"
+    db_file_path: Path = output_folder / db_file_name
+    db_handling: str = "delete"  # One of DB_HANDLING_MODES
+
+    class Config:
+        # Allow arbitrary types for the callable fields
+        arbitrary_types_allowed = True
+
+
+# Example usage with TreeGenome
+from ariel.ec.genotypes.tree.tree_genome import TreeGenome
+
+class TreeEASettings(EASettings[TreeGenome]):
+    """Concrete EA settings for TreeGenome."""
+    
+    def __init__(self, **kwargs):
+        # Set default operators for TreeGenome
+        if 'mutation_operator' not in kwargs:
+            kwargs['mutation_operator'] = self._default_tree_mutation
+        if 'crossover_operator' not in kwargs:
+            kwargs['crossover_operator'] = self._default_tree_crossover
+        if 'create_individual' not in kwargs:
+            kwargs['create_individual'] = TreeGenome.create_individual
+        
+        super().__init__(**kwargs)
+    
+    @staticmethod
+    def _default_tree_mutation(genome: TreeGenome) -> TreeGenome:
+        """Default mutation for TreeGenome."""
+        mutator = TreeGenome.get_mutator_object()
+        return mutator.mutate(genome)
+    
+    @staticmethod
+    def _default_tree_crossover(parent1: TreeGenome, parent2: TreeGenome) -> tuple[TreeGenome, TreeGenome]:
+        """Default crossover for TreeGenome."""
+        crossover = TreeGenome.get_crossover_object()
+        return crossover.crossover(parent1, parent2)
+
+
+# Alternative approach: Factory function for creating EA settings
+def create_ea_settings(
+    genome_type: type[TGenome],
+    mutation_func: Callable[[TGenome], TGenome],
+    crossover_func: Callable[[TGenome, TGenome], tuple[TGenome, TGenome]],
+    create_func: Callable[[], TGenome],
+    **kwargs
+) -> EASettings[TGenome]:
+    """
+    Factory function to create EA settings for any genome type.
+    
+    Args:
+        genome_type: The genome class (e.g., TreeGenome)
+        mutation_func: Function that takes a genome and returns a mutated genome
+        crossover_func: Function that takes two genomes and returns two offspring
+        create_func: Function that creates a new random genome
+        **kwargs: Additional settings to override defaults
+    """
+    settings_data = {
+        'mutation_operator': mutation_func,
+        'crossover_operator': crossover_func,
+        'create_individual': create_func,
+        **kwargs
+    }
+    
+    return EASettings[genome_type](**settings_data)
+
+
+# Example usage:
+if __name__ == "__main__":
+    # Using the concrete TreeEASettings class
+    tree_settings = TreeEASettings(
+        num_of_generations=50,
+        target_population_size=50,
+        target_robot_file_path=Path("examples/target_robots/medium_robot_15.json")
+    )
+    
+    print(f"Settings for {tree_settings.num_of_generations} generations")
+    print(f"Population size: {tree_settings.target_population_size}")
+    print(f"Mutation operator: {tree_settings.mutation_operator}")
+    print(f"Crossover operator: {tree_settings.crossover_operator}")
+    
+    # Using the factory function
+    tree_settings2 = create_ea_settings(
+        genome_type=TreeGenome,
+        mutation_func=TreeGenome.get_mutator_object().mutate,
+        crossover_func=TreeGenome.get_crossover_object().crossover,
+        create_func=TreeGenome.create_individual,
+        num_of_generations=100,
+        target_population_size=100
+    )
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index c349bd4e..dcf46bf1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -37,7 +37,11 @@ Documentation = "https://ariel.readthedocs.io"
 Changelog = "https://github.com/ci-group/ariel/releases"
 
 [dependency-groups]
-dev = ["opencv-stubs>=0.1.0", "types-networkx>=3.5.0.20250918"]
+dev = [
+    "ipykernel>=6.30.1",
+    "opencv-stubs>=0.1.0",
+    "types-networkx>=3.5.0.20250918",
+]
 docs = [
   "jupyter-sphinx>=0.5.3",
   "linkify-it-py>=2.0.3",
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 00000000..3d9939d7
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,15 @@
+matplotlib==3.10.7
+mypyc==0.1.0
+networkx==3.5
+nicegui==3.2.0
+noise==1.2.2
+nox==2025.10.16
+numpy==2.3.4
+Pillow==12.0.0
+pydantic==2.12.3
+pydantic_settings==2.11.0
+rich==14.2.0
+scikit_learn==1.7.2
+setuptools==80.9.0
+SQLAlchemy==2.0.44
+sqlmodel==0.0.27
diff --git a/src/ariel/body_phenotypes/.DS_Store b/src/ariel/body_phenotypes/.DS_Store
index 68be8148..93015b9b 100644
Binary files a/src/ariel/body_phenotypes/.DS_Store and b/src/ariel/body_phenotypes/.DS_Store differ
diff --git a/src/ariel/ec/a000.py b/src/ariel/ec/a000.py
deleted file mode 100644
index 42484398..00000000
--- a/src/ariel/ec/a000.py
+++ /dev/null
@@ -1,156 +0,0 @@
-"""TODO(jmdm): description of script."""
-
-# Standard library
-from collections.abc import Sequence
-from pathlib import Path
-from typing import cast
-
-# Third-party libraries
-import numpy as np
-from pydantic_settings import BaseSettings
-from rich.console import Console
-from rich.traceback import install
-
-# Global constants
-SCRIPT_NAME = __file__.split("/")[-1][:-3]
-CWD = Path.cwd()
-DATA = CWD / "__data__"
-DATA.mkdir(exist_ok=True)
-DB_NAME = "database.db"
-DB_PATH: Path = DATA / DB_NAME
-SEED = 42
-
-# Global functions
-install(width=180)
-console = Console()
-RNG = np.random.default_rng(SEED)
-
-# Type Aliases
-type Integers = Sequence[int]
-type Floats = Sequence[float]
-
-
-class IntegersGeneratorSettings(BaseSettings):
-    integers_endpoint: bool = True
-    choice_replace: bool = True
-    choice_shuffle: bool = False
-
-
-config = IntegersGeneratorSettings()
-
-
-class IntegersGenerator:
-    @staticmethod
-    def integers(
-        low: int,
-        high: int,
-        size: int | Sequence[int] | None = 1,
-        *,
-        endpoint: bool | None = None,
-    ) -> Integers:
-        endpoint = endpoint or config.integers_endpoint
-        generated_values = RNG.integers(
-            low=low,
-            high=high,
-            size=size,
-            endpoint=endpoint,
-        )
-        return cast("Integers", generated_values.astype(int).tolist())
-
-    @staticmethod
-    def choice(
-        value_set: int | Integers,
-        size: int | Sequence[int] | None = 1,
-        probabilities: Sequence[float] | None = None,
-        axis: int = 0,
-        *,
-        replace: bool | None = None,
-        shuffle: bool | None = None,
-    ) -> Integers:
-        replace = replace or config.choice_replace
-        shuffle = shuffle or config.choice_shuffle
-        generated_values = np.array(
-            RNG.choice(
-                a=value_set,
-                size=size,
-                replace=replace,
-                p=probabilities,
-                axis=axis,
-                shuffle=shuffle,
-            ),
-        )
-        return cast("Integers", generated_values.astype(int).tolist())
-
-
-class IntegerMutator:
-    @staticmethod
-    def random_swap(
-        individual: Integers,
-        low: int,
-        high: int,
-        mutation_probability: float,
-    ) -> Integers:
-        shape = np.asarray(individual).shape
-        mutator = RNG.integers(
-            low=low,
-            high=high,
-            size=shape,
-            endpoint=True,
-        )
-        mask = RNG.choice(
-            [True, False],
-            size=shape,
-            p=[mutation_probability, 1 - mutation_probability],
-        )
-        new_genotype = np.where(mask, mutator, individual).astype(int).tolist()
-        return cast("Integers", new_genotype.astype(int).tolist())
-
-    @staticmethod
-    def integer_creep(
-        individual: Integers,
-        span: int,
-        mutation_probability: float,
-    ) -> Integers:
-        # Prep
-        ind_arr = np.array(individual)
-        shape = ind_arr.shape
-
-        # Generate mutation values
-        mutator = RNG.integers(
-            low=1,
-            high=span,
-            size=shape,
-            endpoint=True,
-        )
-
-        # Include negative mutations
-        sub_mask = RNG.choice(
-            [-1, 1],
-            size=shape,
-        )
-
-        # Determine which positions to mutate
-        do_mask = RNG.choice(
-            [1, 0],
-            size=shape,
-            p=[mutation_probability, 1 - mutation_probability],
-        )
-        mutation_mask = mutator * sub_mask * do_mask
-        new_genotype = ind_arr + mutation_mask
-        return cast("Integers", new_genotype.astype(int).tolist())
-                    
-def main() -> None:
-    """Entry point."""
-    console.log(IntegersGenerator.integers(-5, 5, 5))
-    example = IntegersGenerator.choice([1, 3, 4], (2, 5))
-    console.log(example)
-    example2 = IntegerMutator.integer_creep(
-        example,
-        span=1,
-        mutation_probability=1,
-    )
-    console.log(example2)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/src/ariel/ec/a001.py b/src/ariel/ec/a001.py
index edeedf69..177afd61 100644
--- a/src/ariel/ec/a001.py
+++ b/src/ariel/ec/a001.py
@@ -12,7 +12,6 @@
 from sqlmodel import Field, Session, SQLModel, create_engine
 
 # Local libraries
-from ariel.ec.a000 import IntegersGenerator
 
 # Global constants
 SCRIPT_NAME = __file__.split("/")[-1][:-3]
@@ -81,7 +80,7 @@ def fitness(self) -> float:
         return self.fitness_
 
     @fitness.setter
-    def fitness(self, fitness_value: float) -> None:
+    def fitness(self, fitness_value: float | None) -> None:
         if fitness_value is None:
             msg = "Trying to assign `None` to fitness!\n"
             msg += f"--> {self.fitness_value=}"
@@ -121,29 +120,30 @@ def tags(self, tag: dict[JSONType, JSONType]) -> None:
 
 
 def main() -> None:
-    """Entry point."""
-    # Initialize the database
-    engine = init_database()
-
-    # Save data
-    with Session(engine) as session:
-        ind = Individual()
-
-        # Generators
-        ind.genotype = IntegersGenerator.integers(low=0, high=10, size=5)
-
-        # Tags
-        ind.tags = {"a": ["1", 2, 3]}
-        ind.tags = {"b": ("1", 2, 3)}
-        ind.tags = {"c": 1}
-        ind.tags = {"d": True}
-
-        prnt(ind)
-        session.add(ind)
-        session.commit()
-        prnt(ind)
-        session.refresh(ind)
-        prnt(ind)
+    # """Entry point."""
+    # # Initialize the database
+    # engine = init_database()
+
+    # # Save data
+    # with Session(engine) as session:
+    #     ind = Individual()
+
+    #     # Generators
+    #     ind.genotype = IntegersGenerator.integers(low=0, high=10, size=5)
+
+    #     # Tags
+    #     ind.tags = {"a": ["1", 2, 3]}
+    #     ind.tags = {"b": ("1", 2, 3)}
+    #     ind.tags = {"c": 1}
+    #     ind.tags = {"d": True}
+
+    #     prnt(ind)
+    #     session.add(ind)
+    #     session.commit()
+    #     prnt(ind)
+    #     session.refresh(ind)
+    #     prnt(ind)
+    pass
 
 
 if __name__ == "__main__":
diff --git a/src/ariel/ec/a002.py b/src/ariel/ec/a002.py
index 25727c57..aefa607e 100644
--- a/src/ariel/ec/a002.py
+++ b/src/ariel/ec/a002.py
@@ -13,7 +13,6 @@
 from sqlmodel import Session, select
 
 # Local libraries
-from ariel.ec.a000 import IntegersGenerator
 from ariel.ec.a001 import Individual, init_database
 
 # Global constants
diff --git a/src/ariel/ec/a003.py b/src/ariel/ec/a003.py
index b0a8ae62..e3fb216a 100644
--- a/src/ariel/ec/a003.py
+++ b/src/ariel/ec/a003.py
@@ -19,7 +19,6 @@
 from sqlmodel import Session, SQLModel, col, select
 
 # Local libraries
-from ariel.ec.a000 import IntegersGenerator
 from ariel.ec.a001 import Individual
 
 # Third-party libraries
diff --git a/src/ariel/ec/a004.py b/src/ariel/ec/a004.py
index 82e93a3f..b71e09d2 100644
--- a/src/ariel/ec/a004.py
+++ b/src/ariel/ec/a004.py
@@ -22,9 +22,8 @@
 from sqlmodel import Session, SQLModel, col, select
 
 # Local libraries
-from ariel.ec.a000 import IntegerMutator, IntegersGenerator
+from ariel.ec.mutations import IntegerMutator
 from ariel.ec.a001 import Individual
-from ariel.ec.a005 import Crossover
 
 # Global constants
 SEED = 42
@@ -210,7 +209,7 @@ def fetch_population(
                 (Individual.requires_eval != already_evaluated),
             )
         if custom_logic is not None:
-            statement.where(
+            statement = statement.where(
                 *custom_logic,
             )
 
diff --git a/src/ariel/ec/a005.py b/src/ariel/ec/a005.py
deleted file mode 100644
index 1d8e32d6..00000000
--- a/src/ariel/ec/a005.py
+++ /dev/null
@@ -1,75 +0,0 @@
-"""TODO(jmdm): description of script."""
-
-# Standard library
-from pathlib import Path
-
-# Third-party libraries
-import numpy as np
-from rich.console import Console
-from rich.traceback import install
-
-# Local libraries
-from ariel.ec.a000 import IntegersGenerator
-from ariel.ec.a001 import JSONIterable
-
-# Global constants
-SCRIPT_NAME = __file__.split("/")[-1][:-3]
-CWD = Path.cwd()
-DATA = CWD / "__data__"
-DATA.mkdir(exist_ok=True)
-DB_NAME = "database.db"
-DB_PATH: Path = DATA / DB_NAME
-SEED = 42
-
-# Global functions
-install(width=180)
-console = Console()
-RNG = np.random.default_rng(SEED)
-
-
-class Crossover:
-    @staticmethod
-    def one_point(
-        parent_i: JSONIterable,
-        parent_j: JSONIterable,
-    ) -> tuple[JSONIterable, JSONIterable]:
-        # Prep
-        parent_i_arr_shape = np.array(parent_i).shape
-        parent_j_arr_shape = np.array(parent_j).shape
-        parent_i_arr = np.array(parent_i).flatten().copy()
-        parent_j_arr = np.array(parent_j).flatten().copy()
-
-        # Ensure parents have the same length
-        if parent_i_arr_shape != parent_j_arr_shape:
-            msg = "Parents must have the same length"
-            raise ValueError(msg)
-
-        # Select crossover point
-        crossover_point = RNG.integers(0, len(parent_i_arr))
-
-        # Copy over parents
-        child1 = parent_i_arr.copy()
-        child2 = parent_j_arr.copy()
-
-        # Perform crossover
-        child1[crossover_point:] = parent_j_arr[crossover_point:]
-        child2[crossover_point:] = parent_i_arr[crossover_point:]
-
-        # Correct final shape
-        child1 = child1.reshape(parent_i_arr_shape).astype(int).tolist()
-        child2 = child2.reshape(parent_j_arr_shape).astype(int).tolist()
-        return child1, child2
-
-
-def main() -> None:
-    """Entry point."""
-    p1 = IntegersGenerator.integers(-5, 5, (2, 5))
-    p2 = IntegersGenerator.choice([1, 3, 4], (2, 5))
-    console.log(p1, p2)
-
-    c1, c2 = Crossover.one_point(p1, p2)
-    console.log(c1, c2)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/src/ariel/ec/crossovers.py b/src/ariel/ec/crossovers.py
new file mode 100644
index 00000000..5f1e32ee
--- /dev/null
+++ b/src/ariel/ec/crossovers.py
@@ -0,0 +1,531 @@
+"""TODO(jmdm): description of script."""
+from __future__ import annotations
+
+# Standard library
+from abc import ABC, abstractmethod
+from pathlib import Path
+import random
+
+# Third-party libraries
+import numpy as np
+from rich.console import Console
+from rich.traceback import install
+
+from typing import TYPE_CHECKING
+
+# Local libraries
+from ariel.ec.genotypes.lsystem.l_system_genotype import LSystemDecoder
+if TYPE_CHECKING:
+    from ariel.ec.genotypes.genotype import Genotype
+    from ariel.ec.genotypes.tree.tree_genome import TreeGenome
+
+# Global constants
+SCRIPT_NAME = __file__.split("/")[-1][:-3]
+CWD = Path.cwd()
+DATA = CWD / "__data__"
+DATA.mkdir(exist_ok=True)
+DB_NAME = "database.db"
+DB_PATH: Path = DATA / DB_NAME
+SEED = 42
+
+# Global functions
+install(width=180)
+console = Console()
+RNG = np.random.default_rng(SEED)
+
+class Crossover(ABC):
+    crossovers_mapping: dict[str, function] = NotImplemented
+    which_crossover: str = ""
+
+    @classmethod
+    def set_which_crossover(cls, crossover_type: str) -> None:
+        cls.which_crossover = crossover_type
+
+    def __init_subclass__(cls):
+        super().__init_subclass__()
+        cls.crossovers_mapping = {
+            name: getattr(cls, name)
+            for name, val in cls.__dict__.items()
+            if isinstance(val, staticmethod)
+        }
+
+    @classmethod
+    def __call__(
+        cls,
+        parent_i: Genotype,
+        parent_j: Genotype,
+        **kwargs: dict,
+    ) -> tuple[Genotype, Genotype]:
+        """Perform crossover on two genotypes.
+
+        Parameters
+        ----------
+        parent_i : Genotype
+            The first parent genotype (list or nested list of integers).
+        parent_j : Genotype
+            The second parent genotype (list or nested list of integers).
+
+        Returns
+        -------
+        tuple[Genotype, Genotype]
+            Two child genotypes resulting from the crossover.
+        """
+        if cls.which_crossover in cls.crossovers_mapping:
+            return cls.crossovers_mapping[cls.which_crossover](
+                parent_i,
+                parent_j,
+                **kwargs
+            )
+        else:
+            msg = f"Crossover type '{cls.which_crossover}' not recognized."
+            raise ValueError(msg)
+
+class IntegerCrossover(Crossover):
+    @staticmethod
+    def one_point(
+        parent_i: Genotype,
+        parent_j: Genotype,
+    ) -> tuple[Genotype, Genotype]:
+        # Prep
+        parent_i_arr_shape = np.array(parent_i).shape
+        parent_j_arr_shape = np.array(parent_j).shape
+        parent_i_arr = np.array(parent_i).flatten().copy()
+        parent_j_arr = np.array(parent_j).flatten().copy()
+
+        # Ensure parents have the same length
+        if parent_i_arr_shape != parent_j_arr_shape:
+            msg = "Parents must have the same length"
+            raise ValueError(msg)
+
+        # Select crossover point
+        crossover_point = RNG.integers(0, len(parent_i_arr))
+
+        # Copy over parents
+        child1 = parent_i_arr.copy()
+        child2 = parent_j_arr.copy()
+
+        # Perform crossover
+        child1[crossover_point:] = parent_j_arr[crossover_point:]
+        child2[crossover_point:] = parent_i_arr[crossover_point:]
+
+        # Correct final shape
+        child1 = child1.reshape(parent_i_arr_shape).astype(int).tolist()
+        child2 = child2.reshape(parent_j_arr_shape).astype(int).tolist()
+        return child1, child2
+
+class TreeCrossover(Crossover):
+    @staticmethod
+    def koza_default(
+        parent_i: TreeGenome,
+        parent_j: TreeGenome,
+        koza_internal_node_prob: float = 0.9,
+    ) -> tuple[TreeGenome, TreeGenome]:
+        """
+        Koza default:
+            -   In Parent A: choose an internal node with high probability (e.g., 90%) excluding root.
+                Falls back to any node if A has no internal nodes.
+            -   In Parent B: choose any node uniformly (internal or terminal).
+
+        Forcing at least one internal node increases the chance you actually change structure
+        (not just swapping a leaf for a leaf), while letting the other parent be unrestricted adds variety.
+        """
+        parent_i_root, parent_j_root = parent_i.root, parent_j.root
+
+        nodes_a = parent_i_root.get_all_nodes(exclude_root=True)
+        nodes_b = parent_j_root.get_all_nodes(exclude_root=True)
+
+        # If either tree is just a root, return copies of parents
+        if not nodes_a or not nodes_b:
+            return parent_i.copy(), parent_j.copy()
+
+        parent_i_internal_nodes = parent_i_root.get_internal_nodes(mode="dfs", exclude_root=True)
+
+        if RNG.random() > koza_internal_node_prob and parent_i_internal_nodes:
+            node_a = RNG.choice(parent_i_internal_nodes)
+        else:
+            node_a = RNG.choice(parent_i_root.get_all_nodes(mode="dfs", exclude_root=True))
+
+        parent_j_all_nodes = parent_j_root.get_all_nodes()
+        node_b = RNG.choice(parent_j_all_nodes)
+        if node_a is None or node_b is None:
+            # If either tree is just a root, return copies of parents
+            return parent_i.copy(), parent_j.copy()
+
+        parent_i_old = parent_i.copy()
+        parent_j_old = parent_j.copy()
+        child1 = parent_i
+        child2 = parent_j
+
+        with child1.root.enable_replacement():
+            child1.root.replace_node(node_a, node_b)
+        with child2.root.enable_replacement():
+            child2.root.replace_node(node_b, node_a)
+
+        parent_i = parent_i_old
+        parent_j = parent_j_old
+        return child1, child2
+    
+    @staticmethod
+    def normal(
+        parent_i: TreeGenome,
+        parent_j: TreeGenome,
+    ) -> tuple[TreeGenome, TreeGenome]:
+        """
+        Normal tree crossover:
+            - Pick a random node from Parent A (uniform over all nodes).
+            - Pick a random node from Parent B (uniform over all nodes).
+            - Swap the selected subtrees.
+
+        Returns two children produced by swapping the chosen subtrees.
+        """
+        parent_i_root, parent_j_root = parent_i.root, parent_j.root
+
+        nodes_a = parent_i_root.get_all_nodes(exclude_root=True)
+        nodes_b = parent_j_root.get_all_nodes(exclude_root=True)
+
+        # If either tree is just a root, return copies of parents
+        if not nodes_a or not nodes_b:
+            return parent_i.copy(), parent_j.copy()
+
+        # Uniformly choose any node (root, internal, or leaf)
+        node_a = RNG.choice(nodes_a)
+        node_b = RNG.choice(nodes_b)
+
+        # Preserve originals (same pattern as in koza_default)
+        parent_i_old = parent_i.copy()
+        parent_j_old = parent_j.copy()
+        child1 = parent_i
+        child2 = parent_j
+
+        # Perform the swap
+        with child1.root.enable_replacement():
+            child1.root.replace_node(node_a, node_b)
+        with child2.root.enable_replacement():
+            child2.root.replace_node(node_b, node_a)
+
+        # Restore parent handles for caller (as in your koza_default)
+        parent_i = parent_i_old
+        parent_j = parent_j_old
+        return child1, child2
+    
+class LSystemCrossover(Crossover):
+    @staticmethod
+    def crossover_uniform_rules_lsystem(lsystem_parent1,lsystem_parent2,mutation_rate):
+        axiom_offspring1="C"
+        axiom_offspring2="C"
+        rules_offspring1={}
+        rules_offspring2={}
+        iter_offspring1=0
+        iter_offspring2=0
+        if random.random()>mutation_rate:
+            rules_offspring1['C']=lsystem_parent2.rules['C']
+            rules_offspring2['C']=lsystem_parent1.rules['C']
+            iter_offspring1+=lsystem_parent2.iterations
+            iter_offspring2+=lsystem_parent1.iterations
+        else:
+            rules_offspring1['C']=lsystem_parent1.rules['C']
+            rules_offspring2['C']=lsystem_parent2.rules['C']
+            iter_offspring1+=lsystem_parent1.iterations
+            iter_offspring2+=lsystem_parent2.iterations
+        if random.random()>mutation_rate:
+            rules_offspring1['B']=lsystem_parent2.rules['B']
+            rules_offspring2['B']=lsystem_parent1.rules['B']
+            iter_offspring1+=lsystem_parent2.iterations
+            iter_offspring2+=lsystem_parent1.iterations
+        else:
+            rules_offspring1['B']=lsystem_parent1.rules['B']
+            rules_offspring2['B']=lsystem_parent2.rules['B']
+            iter_offspring1+=lsystem_parent1.iterations
+            iter_offspring2+=lsystem_parent2.iterations
+        if random.random()>mutation_rate:
+            rules_offspring1['H']=lsystem_parent2.rules['H']
+            rules_offspring2['H']=lsystem_parent1.rules['H']
+            iter_offspring1+=lsystem_parent2.iterations
+            iter_offspring2+=lsystem_parent1.iterations
+        else:
+            rules_offspring1['H']=lsystem_parent1.rules['H']
+            rules_offspring2['H']=lsystem_parent2.rules['H']
+            iter_offspring1+=lsystem_parent1.iterations
+            iter_offspring2+=lsystem_parent2.iterations
+        if random.random()>mutation_rate:
+            rules_offspring1['N']=lsystem_parent2.rules['N']
+            rules_offspring2['N']=lsystem_parent1.rules['N']
+            iter_offspring1+=lsystem_parent2.iterations
+            iter_offspring2+=lsystem_parent1.iterations
+        else:
+            rules_offspring1['N']=lsystem_parent1.rules['N']
+            rules_offspring2['N']=lsystem_parent2.rules['N']
+            iter_offspring1+=lsystem_parent1.iterations
+            iter_offspring2+=lsystem_parent2.iterations
+        iteration_offspring1=int(iter_offspring1/4)
+        iteration_offspring2=int(iter_offspring2/4)
+        offspring1=LSystemDecoder(axiom_offspring1,rules_offspring1,iteration_offspring1,lsystem_parent1.max_elements,lsystem_parent1.max_depth,lsystem_parent1.verbose)
+        offspring2=LSystemDecoder(axiom_offspring2,rules_offspring2,iteration_offspring2,lsystem_parent2.max_elements,lsystem_parent2.max_depth,lsystem_parent2.verbose)
+        return offspring1,offspring2
+
+    @staticmethod
+    def crossover_uniform_genes_lsystem(lsystem_parent1,lsystem_parent2,mutation_rate):
+        axiom_offspring1="C"
+        axiom_offspring2="C"
+        rules_offspring1={}
+        rules_offspring2={}
+        iter_offspring1=0
+        iter_offspring2=0
+
+        rules_parent1 = lsystem_parent1.rules["C"].split()
+        rules_parent2 = lsystem_parent2.rules["C"].split()
+        enh_parent1 = []
+        enh_parent2 = []
+        i = 0
+        while i < len(rules_parent1):
+            if rules_parent1[i][:4] in ['addf','addk','addl','addr','addb','addt']:
+                new_token= rules_parent1[i] + " " + rules_parent1[i+1]
+                enh_parent1.append(new_token)
+                i+=1
+            if rules_parent1[i][:4] in ['movf','movk','movl','movr','movb','movt']:
+                enh_parent1.append(rules_parent1[i])
+            if rules_parent1[i]=='C':
+                enh_parent1.append(rules_parent1[i])
+            i+=1 
+        i = 0
+        while i < len(rules_parent2):
+            if rules_parent2[i][:4] in ['addf','addk','addl','addr','addb','addt']:
+                new_token= rules_parent2[i] + " " + rules_parent2[i+1]
+                enh_parent2.append(new_token)
+                i+=1
+            if rules_parent2[i][:4] in ['movf','movk','movl','movr','movb','movt']:
+                enh_parent2.append(rules_parent2[i])
+            if rules_parent2[i]=='C':
+                enh_parent2.append(rules_parent2[i])
+            i+=1 
+        r_offspring1=""
+        r_offspring2=""
+        le_common = min(len(enh_parent1),len(enh_parent2))
+        for i in range(0,le_common):
+            if random.random()>mutation_rate:
+                r_offspring1+=enh_parent2[i]+" "
+                r_offspring2+=enh_parent1[i]+" "
+            else:
+                r_offspring1+=enh_parent1[i]+" "
+                r_offspring2+=enh_parent2[i]+" "
+        if len(enh_parent1)>le_common:
+            for j in range(le_common,len(enh_parent1)):
+                if random.random()>mutation_rate:
+                    r_offspring1+=enh_parent1[j]+" "
+                else:   
+                    r_offspring2+=enh_parent1[j]+" "
+        if len(enh_parent2)>le_common:
+            for j in range(le_common,len(enh_parent2)):
+                if random.random()>mutation_rate:
+                    r_offspring1+=enh_parent2[j]+" "
+                else:
+                    r_offspring2+=enh_parent2[j]+" "
+        rules_offspring1['C']=r_offspring1
+        rules_offspring2['C']=r_offspring2
+
+        rules_parent1 = lsystem_parent1.rules["B"].split()
+        rules_parent2 = lsystem_parent2.rules["B"].split()
+        enh_parent1 = []
+        enh_parent2 = []
+        i = 0
+        while i < len(rules_parent1):
+            if rules_parent1[i][:4] in ['addf','addk','addl','addr','addb','addt']:
+                new_token= rules_parent1[i] + " " + rules_parent1[i+1]
+                enh_parent1.append(new_token)
+                i+=1
+            if rules_parent1[i][:4] in ['movf','movk','movl','movr','movb','movt']:
+                enh_parent1.append(rules_parent1[i])
+            if rules_parent1[i]=='B':
+                enh_parent1.append(rules_parent1[i])
+            i+=1 
+        i = 0
+        while i < len(rules_parent2):
+            if rules_parent2[i][:4] in ['addf','addk','addl','addr','addb','addt']:
+                new_token= rules_parent2[i] + " " + rules_parent2[i+1]
+                enh_parent2.append(new_token)
+                i+=1
+            if rules_parent2[i][:4] in ['movf','movk','movl','movr','movb','movt']:
+                enh_parent2.append(rules_parent2[i])
+            if rules_parent2[i]=='B':
+                enh_parent2.append(rules_parent2[i])
+            i+=1 
+        r_offspring1=""
+        r_offspring2=""
+        le_common = min(len(enh_parent1),len(enh_parent2))
+        for i in range(0,le_common):
+            if random.random()>mutation_rate:
+                r_offspring1+=enh_parent2[i]+" "
+                r_offspring2+=enh_parent1[i]+" "
+            else:
+                r_offspring1+=enh_parent1[i]+" "
+                r_offspring2+=enh_parent2[i]+" "
+        if len(enh_parent1)>le_common:
+            for j in range(le_common,len(enh_parent1)):
+                if random.random()>mutation_rate:
+                    r_offspring1+=enh_parent1[j]+" "
+                else:   
+                    r_offspring2+=enh_parent1[j]+" "
+        if len(enh_parent2)>le_common:
+            for j in range(le_common,len(enh_parent2)):
+                if random.random()>mutation_rate:
+                    r_offspring1+=enh_parent2[j]+" "
+                else:
+                    r_offspring2+=enh_parent2[j]+" "
+        rules_offspring1['B']=r_offspring1
+        rules_offspring2['B']=r_offspring2
+
+        rules_parent1 = lsystem_parent1.rules["H"].split()
+        rules_parent2 = lsystem_parent2.rules["H"].split()
+        enh_parent1 = []
+        enh_parent2 = []
+        i = 0
+        while i < len(rules_parent1):
+            if rules_parent1[i][:4] in ['addf','addk','addl','addr','addb','addt']:
+                new_token= rules_parent1[i] + " " + rules_parent1[i+1]
+                enh_parent1.append(new_token)
+                i+=1
+            if rules_parent1[i][:4] in ['movf','movk','movl','movr','movb','movt']:
+                enh_parent1.append(rules_parent1[i])
+            if rules_parent1[i]=='H':
+                enh_parent1.append(rules_parent1[i])
+            i+=1 
+        i = 0
+        while i < len(rules_parent2):
+            if rules_parent2[i][:4] in ['addf','addk','addl','addr','addb','addt']:
+                new_token= rules_parent2[i] + " " + rules_parent2[i+1]
+                enh_parent2.append(new_token)
+                i+=1
+            if rules_parent2[i][:4] in ['movf','movk','movl','movr','movb','movt']:
+                enh_parent2.append(rules_parent2[i])
+            if rules_parent2[i]=='H':
+                enh_parent2.append(rules_parent2[i])
+            i+=1 
+        r_offspring1=""
+        r_offspring2=""
+        le_common = min(len(enh_parent1),len(enh_parent2))
+        for i in range(0,le_common):
+            if random.random()>mutation_rate:
+                r_offspring1+=enh_parent2[i]+" "
+                r_offspring2+=enh_parent1[i]+" "
+            else:
+                r_offspring1+=enh_parent1[i]+" "
+                r_offspring2+=enh_parent2[i]+" "
+        if len(enh_parent1)>le_common:
+            for j in range(le_common,len(enh_parent1)):
+                if random.random()>mutation_rate:
+                    r_offspring1+=enh_parent1[j]+" "
+                else:   
+                    r_offspring2+=enh_parent1[j]+" "
+        if len(enh_parent2)>le_common:
+            for j in range(le_common,len(enh_parent2)):
+                if random.random()>mutation_rate:
+                    r_offspring1+=enh_parent2[j]+" "
+                else:
+                    r_offspring2+=enh_parent2[j]+" "
+        rules_offspring1['H']=r_offspring1
+        rules_offspring2['H']=r_offspring2
+
+        rules_parent1 = lsystem_parent1.rules["N"].split()
+        rules_parent2 = lsystem_parent2.rules["N"].split()
+        enh_parent1 = []
+        enh_parent2 = []
+        i = 0
+        while i < len(rules_parent1):
+            if rules_parent1[i][:4] in ['addf','addk','addl','addr','addb','addt']:
+                new_token= rules_parent1[i] + " " + rules_parent1[i+1]
+                enh_parent1.append(new_token)
+                i+=1
+            if rules_parent1[i][:4] in ['movf','movk','movl','movr','movb','movt']:
+                enh_parent1.append(rules_parent1[i])
+            if rules_parent1[i]=='N':
+                enh_parent1.append(rules_parent1[i])
+            i+=1 
+        i = 0
+        while i < len(rules_parent2):
+            if rules_parent2[i][:4] in ['addf','addk','addl','addr','addb','addt']:
+                new_token= rules_parent2[i] + " " + rules_parent2[i+1]
+                enh_parent2.append(new_token)
+                i+=1
+            if rules_parent2[i][:4] in ['movf','movk','movl','movr','movb','movt']:
+                enh_parent2.append(rules_parent2[i])
+            if rules_parent2[i]=='N':
+                enh_parent2.append(rules_parent2[i])
+            i+=1 
+        r_offspring1=""
+        r_offspring2=""
+        le_common = min(len(enh_parent1),len(enh_parent2))
+        for i in range(0,le_common):
+            if random.random()>mutation_rate:
+                r_offspring1+=enh_parent2[i]+" "
+                r_offspring2+=enh_parent1[i]+" "
+            else:
+                r_offspring1+=enh_parent1[i]+" "
+                r_offspring2+=enh_parent2[i]+" "
+        if len(enh_parent1)>le_common:
+            for j in range(le_common,len(enh_parent1)):
+                if random.random()>mutation_rate:
+                    r_offspring1+=enh_parent1[j]+" "
+                else:   
+                    r_offspring2+=enh_parent1[j]+" "
+        if len(enh_parent2)>le_common:
+            for j in range(le_common,len(enh_parent2)):
+                if random.random()>mutation_rate:
+                    r_offspring1+=enh_parent2[j]+" "
+                else:
+                    r_offspring2+=enh_parent2[j]+" "
+        rules_offspring1['N']=r_offspring1
+        rules_offspring2['N']=r_offspring2
+
+        iter_offspring1+=lsystem_parent2.iterations
+        iter_offspring2+=lsystem_parent1.iterations
+        offspring1=LSystemDecoder(axiom_offspring1,rules_offspring1,iter_offspring1,lsystem_parent1.max_elements,lsystem_parent1.max_depth,lsystem_parent1.verbose)
+        offspring2=LSystemDecoder(axiom_offspring2,rules_offspring2,iter_offspring2,lsystem_parent2.max_elements,lsystem_parent2.max_depth,lsystem_parent2.verbose)
+        return offspring1,offspring2
+
+
+def tree_main():
+    import ariel.body_phenotypes.robogen_lite.config as config
+    from ariel.ec.genotypes.tree.tree_genome import TreeNode, TreeGenome
+
+    # Create first tree
+    genome1 = TreeGenome()
+    genome1.root = TreeNode(
+        config.ModuleInstance(type=config.ModuleType.CORE, rotation=config.ModuleRotationsIdx.DEG_0, links={}))
+    genome1.root.front = TreeNode(
+        config.ModuleInstance(type=config.ModuleType.BRICK, rotation=config.ModuleRotationsIdx.DEG_90, links={}))
+    genome1.root.back = TreeNode(
+        config.ModuleInstance(type=config.ModuleType.HINGE, rotation=config.ModuleRotationsIdx.DEG_45, links={}))
+
+    # Create second tree
+    genome2 = TreeGenome()
+    genome2.root = TreeNode(
+        config.ModuleInstance(type=config.ModuleType.CORE, rotation=config.ModuleRotationsIdx.DEG_0, links={}))
+    genome2.root.right = TreeNode(
+        config.ModuleInstance(type=config.ModuleType.BRICK, rotation=config.ModuleRotationsIdx.DEG_180, links={}))
+    genome2.root.back = TreeNode(
+        config.ModuleInstance(type=config.ModuleType.HINGE, rotation=config.ModuleRotationsIdx.DEG_270, links={}))
+
+    console.log("Parent 1:", genome1)
+    console.log("Parent 2:", genome2)
+
+    genome2.root.replace_node(genome1, genome2)
+
+    # Perform crossover
+    child1, child2 = TreeCrossover.koza_default(genome1, genome2)
+
+    console.log("Child 1:", child1)
+    console.log("Child 2:", child2)
+
+
+def main() -> None:
+    """Entry point."""
+    p1 = IntegersGenerator.integers(-5, 5, (2, 5))
+    p2 = IntegersGenerator.choice([1, 3, 4], (2, 5))
+    console.log(p1, p2)
+
+    c1, c2 = Crossover.one_point(p1, p2)
+    console.log(c1, c2)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/ariel/ec/ec_l_system.py b/src/ariel/ec/ec_l_system.py
new file mode 100644
index 00000000..5bd7e5dd
--- /dev/null
+++ b/src/ariel/ec/ec_l_system.py
@@ -0,0 +1,436 @@
+"""Example of L-system-based evolutionary computing algorithm for modular robot graphs.
+
+Author:     omn
+Date:       2025-09-26
+Py Ver:     3.12
+OS:         macOS Tahoe 26
+Status:     Prototype
+
+Notes
+-----
+
+References
+----------
+
+"""
+
+# Standard library
+
+import json
+import re
+from pathlib import Path
+from typing import Any, Callable, Dict, Optional
+from enum import Enum
+import random
+
+
+# Local libraries
+from ariel.ec.genotypes.lsystem.l_system_genotype import LSystemDecoder
+
+SEED = 42
+DPI = 300
+
+def mutate_one_point_lsystem(lsystem,mut_rate,add_temperature=0.5):
+    op_completed = ""
+    if random.random()<mut_rate:
+        action=random.choices(['add_rule','rm_rule'],weights=[add_temperature,1-add_temperature])[0]
+        rules = lsystem.rules
+        rule_to_change=random.choice(range(0,len(rules)))
+        rl_tmp = list(rules.values())[rule_to_change]
+        splitted_rules=rl_tmp.split()
+        gene_to_change=random.choice(range(0,len(splitted_rules)))
+        match action:
+            case 'add_rule':
+                operator=random.choice(['addf','addk','addl','addr','addb','addt','movf','movk','movl','movr','movt','movb'])
+                if operator in ['addf','addk','addl','addr','addb','addt']:
+                    if splitted_rules[gene_to_change][:4] in ['addf','addk','addl','addr','addb','addt']:
+                        rotation = random.choice([0,45,90,135,180,225,270])
+                        op_to_add=operator+"("+str(rotation)+")"
+                        item_to_add=random.choice(['B','H','N'])
+                        splitted_rules.insert(gene_to_change+2,item_to_add)
+                        splitted_rules.insert(gene_to_change+2,op_to_add)
+                        op_completed="ADDED : "+op_to_add+" "+item_to_add
+                    elif splitted_rules[gene_to_change][:4] in ['movf','movk','movl','movr','movb','movt']:
+                        rotation = random.choice([0,45,90,135,180,225,270])
+                        op_to_add=operator+"("+str(rotation)+")"
+                        item_to_add=random.choice(['B','H','N'])
+                        splitted_rules.insert(gene_to_change,item_to_add)
+                        splitted_rules.insert(gene_to_change,op_to_add)
+                        op_completed="ADDED : "+op_to_add+" "+item_to_add
+                    elif splitted_rules[gene_to_change] in ['C','B','H','N']:
+                        rotation = random.choice([0,45,90,135,180,225,270])
+                        op_to_add=operator+"("+str(rotation)+")"
+                        item_to_add=random.choice(['B','H','N'])
+                        splitted_rules.insert(gene_to_change+1,item_to_add)
+                        splitted_rules.insert(gene_to_change+1,op_to_add)
+                        op_completed="ADDED : "+op_to_add+" "+item_to_add
+                if operator in ['movf','movk','movl','movr','movb','movt']:
+                    if splitted_rules[gene_to_change][:4] in ['addf','addk','addl','addr','addb','addt']:
+                        splitted_rules.insert(gene_to_change+2,operator)
+                        op_completed="ADDED : "+operator
+                    elif splitted_rules[gene_to_change][:4] in ['movf','movk','movl','movr','movb','movt']:
+                        splitted_rules.insert(gene_to_change,operator)
+                        op_completed="ADDED : "+operator
+                    elif splitted_rules[gene_to_change] in ['C','B','H','N']:
+                        splitted_rules.insert(gene_to_change+1,operator)
+                        op_completed="ADDED : "+operator
+            case 'rm_rule':
+                if splitted_rules[gene_to_change][:4] in ['addf','addk','addl','addr','addb','addt']:
+                    op_completed="REMOVED : "+splitted_rules[gene_to_change]+" "+splitted_rules[gene_to_change+1]
+                    splitted_rules.pop(gene_to_change)
+                    splitted_rules.pop(gene_to_change)
+                elif splitted_rules[gene_to_change] in ['H','B','N']:
+                    op_completed="REMOVED : "+splitted_rules[gene_to_change-1]+" "+splitted_rules[gene_to_change]
+                    if gene_to_change-1>=0:
+                        splitted_rules.pop(gene_to_change-1)
+                        splitted_rules.pop(gene_to_change-1)
+                elif splitted_rules[gene_to_change][:4] in ['movf','movk','movl','movr','movt','movb']:
+                    op_completed="REMOVED : "+splitted_rules[gene_to_change]
+                    splitted_rules.pop(gene_to_change)
+        new_rule = ""
+        for j in range(0,len(splitted_rules)):
+            new_rule+=splitted_rules[j]+" "
+        if new_rule!="":
+            lsystem.rules[list(rules.keys())[rule_to_change]]=new_rule
+        else:
+            lsystem.rules[list(rules.keys())[rule_to_change]]=lsystem.rules[list(rules.keys())[rule_to_change]]
+    return op_completed
+
+def crossover_uniform_rules_lsystem(lsystem_parent1,lsystem_parent2,mutation_rate):
+    axiom_offspring1="C"
+    axiom_offspring2="C"
+    rules_offspring1={}
+    rules_offspring2={}
+    iter_offspring1=0
+    iter_offspring2=0
+    if random.random()>mutation_rate:
+        rules_offspring1['C']=lsystem_parent2.rules['C']
+        rules_offspring2['C']=lsystem_parent1.rules['C']
+        iter_offspring1+=lsystem_parent2.iterations
+        iter_offspring2+=lsystem_parent1.iterations
+    else:
+        rules_offspring1['C']=lsystem_parent1.rules['C']
+        rules_offspring2['C']=lsystem_parent2.rules['C']
+        iter_offspring1+=lsystem_parent1.iterations
+        iter_offspring2+=lsystem_parent2.iterations
+    if random.random()>mutation_rate:
+        rules_offspring1['B']=lsystem_parent2.rules['B']
+        rules_offspring2['B']=lsystem_parent1.rules['B']
+        iter_offspring1+=lsystem_parent2.iterations
+        iter_offspring2+=lsystem_parent1.iterations
+    else:
+        rules_offspring1['B']=lsystem_parent1.rules['B']
+        rules_offspring2['B']=lsystem_parent2.rules['B']
+        iter_offspring1+=lsystem_parent1.iterations
+        iter_offspring2+=lsystem_parent2.iterations
+    if random.random()>mutation_rate:
+        rules_offspring1['H']=lsystem_parent2.rules['H']
+        rules_offspring2['H']=lsystem_parent1.rules['H']
+        iter_offspring1+=lsystem_parent2.iterations
+        iter_offspring2+=lsystem_parent1.iterations
+    else:
+        rules_offspring1['H']=lsystem_parent1.rules['H']
+        rules_offspring2['H']=lsystem_parent2.rules['H']
+        iter_offspring1+=lsystem_parent1.iterations
+        iter_offspring2+=lsystem_parent2.iterations
+    if random.random()>mutation_rate:
+        rules_offspring1['N']=lsystem_parent2.rules['N']
+        rules_offspring2['N']=lsystem_parent1.rules['N']
+        iter_offspring1+=lsystem_parent2.iterations
+        iter_offspring2+=lsystem_parent1.iterations
+    else:
+        rules_offspring1['N']=lsystem_parent1.rules['N']
+        rules_offspring2['N']=lsystem_parent2.rules['N']
+        iter_offspring1+=lsystem_parent1.iterations
+        iter_offspring2+=lsystem_parent2.iterations
+    iteration_offspring1=int(iter_offspring1/4)
+    iteration_offspring2=int(iter_offspring2/4)
+    offspring1=LSystemDecoder(axiom_offspring1,rules_offspring1,iteration_offspring1,lsystem_parent1.max_elements,lsystem_parent1.max_depth,lsystem_parent1.verbose)
+    offspring2=LSystemDecoder(axiom_offspring2,rules_offspring2,iteration_offspring2,lsystem_parent2.max_elements,lsystem_parent2.max_depth,lsystem_parent2.verbose)
+    return offspring1,offspring2
+
+
+def crossover_uniform_genes_lsystem(lsystem_parent1,lsystem_parent2,mutation_rate):
+    axiom_offspring1="C"
+    axiom_offspring2="C"
+    rules_offspring1={}
+    rules_offspring2={}
+    iter_offspring1=0
+    iter_offspring2=0
+
+    rules_parent1 = lsystem_parent1.rules["C"].split()
+    rules_parent2 = lsystem_parent2.rules["C"].split()
+    enh_parent1 = []
+    enh_parent2 = []
+    i = 0
+    while i < len(rules_parent1):
+        if rules_parent1[i][:4] in ['addf','addk','addl','addr','addb','addt']:
+            new_token= rules_parent1[i] + " " + rules_parent1[i+1]
+            enh_parent1.append(new_token)
+            i+=1
+        if rules_parent1[i][:4] in ['movf','movk','movl','movr','movb','movt']:
+            enh_parent1.append(rules_parent1[i])
+        if rules_parent1[i]=='C':
+            enh_parent1.append(rules_parent1[i])
+        i+=1 
+    i = 0
+    while i < len(rules_parent2):
+        if rules_parent2[i][:4] in ['addf','addk','addl','addr','addb','addt']:
+            new_token= rules_parent2[i] + " " + rules_parent2[i+1]
+            enh_parent2.append(new_token)
+            i+=1
+        if rules_parent2[i][:4] in ['movf','movk','movl','movr','movb','movt']:
+            enh_parent2.append(rules_parent2[i])
+        if rules_parent2[i]=='C':
+            enh_parent2.append(rules_parent2[i])
+        i+=1 
+    r_offspring1=""
+    r_offspring2=""
+    le_common = min(len(enh_parent1),len(enh_parent2))
+    for i in range(0,le_common):
+        if random.random()>mutation_rate:
+            r_offspring1+=enh_parent2[i]+" "
+            r_offspring2+=enh_parent1[i]+" "
+        else:
+            r_offspring1+=enh_parent1[i]+" "
+            r_offspring2+=enh_parent2[i]+" "
+    if len(enh_parent1)>le_common:
+        for j in range(le_common,len(enh_parent1)):
+            if random.random()>mutation_rate:
+                r_offspring1+=enh_parent1[j]+" "
+            else:   
+                r_offspring2+=enh_parent1[j]+" "
+    if len(enh_parent2)>le_common:
+        for j in range(le_common,len(enh_parent2)):
+            if random.random()>mutation_rate:
+                r_offspring1+=enh_parent2[j]+" "
+            else:
+                r_offspring2+=enh_parent2[j]+" "
+    rules_offspring1['C']=r_offspring1
+    rules_offspring2['C']=r_offspring2
+
+    rules_parent1 = lsystem_parent1.rules["B"].split()
+    rules_parent2 = lsystem_parent2.rules["B"].split()
+    enh_parent1 = []
+    enh_parent2 = []
+    i = 0
+    while i < len(rules_parent1):
+        if rules_parent1[i][:4] in ['addf','addk','addl','addr','addb','addt']:
+            new_token= rules_parent1[i] + " " + rules_parent1[i+1]
+            enh_parent1.append(new_token)
+            i+=1
+        if rules_parent1[i][:4] in ['movf','movk','movl','movr','movb','movt']:
+            enh_parent1.append(rules_parent1[i])
+        if rules_parent1[i]=='B':
+            enh_parent1.append(rules_parent1[i])
+        i+=1 
+    i = 0
+    while i < len(rules_parent2):
+        if rules_parent2[i][:4] in ['addf','addk','addl','addr','addb','addt']:
+            new_token= rules_parent2[i] + " " + rules_parent2[i+1]
+            enh_parent2.append(new_token)
+            i+=1
+        if rules_parent2[i][:4] in ['movf','movk','movl','movr','movb','movt']:
+            enh_parent2.append(rules_parent2[i])
+        if rules_parent2[i]=='B':
+            enh_parent2.append(rules_parent2[i])
+        i+=1 
+    r_offspring1=""
+    r_offspring2=""
+    le_common = min(len(enh_parent1),len(enh_parent2))
+    for i in range(0,le_common):
+        if random.random()>mutation_rate:
+            r_offspring1+=enh_parent2[i]+" "
+            r_offspring2+=enh_parent1[i]+" "
+        else:
+            r_offspring1+=enh_parent1[i]+" "
+            r_offspring2+=enh_parent2[i]+" "
+    if len(enh_parent1)>le_common:
+        for j in range(le_common,len(enh_parent1)):
+            if random.random()>mutation_rate:
+                r_offspring1+=enh_parent1[j]+" "
+            else:   
+                r_offspring2+=enh_parent1[j]+" "
+    if len(enh_parent2)>le_common:
+        for j in range(le_common,len(enh_parent2)):
+            if random.random()>mutation_rate:
+                r_offspring1+=enh_parent2[j]+" "
+            else:
+                r_offspring2+=enh_parent2[j]+" "
+    rules_offspring1['B']=r_offspring1
+    rules_offspring2['B']=r_offspring2
+
+    rules_parent1 = lsystem_parent1.rules["H"].split()
+    rules_parent2 = lsystem_parent2.rules["H"].split()
+    enh_parent1 = []
+    enh_parent2 = []
+    i = 0
+    while i < len(rules_parent1):
+        if rules_parent1[i][:4] in ['addf','addk','addl','addr','addb','addt']:
+            new_token= rules_parent1[i] + " " + rules_parent1[i+1]
+            enh_parent1.append(new_token)
+            i+=1
+        if rules_parent1[i][:4] in ['movf','movk','movl','movr','movb','movt']:
+            enh_parent1.append(rules_parent1[i])
+        if rules_parent1[i]=='H':
+            enh_parent1.append(rules_parent1[i])
+        i+=1 
+    i = 0
+    while i < len(rules_parent2):
+        if rules_parent2[i][:4] in ['addf','addk','addl','addr','addb','addt']:
+            new_token= rules_parent2[i] + " " + rules_parent2[i+1]
+            enh_parent2.append(new_token)
+            i+=1
+        if rules_parent2[i][:4] in ['movf','movk','movl','movr','movb','movt']:
+            enh_parent2.append(rules_parent2[i])
+        if rules_parent2[i]=='H':
+            enh_parent2.append(rules_parent2[i])
+        i+=1 
+    r_offspring1=""
+    r_offspring2=""
+    le_common = min(len(enh_parent1),len(enh_parent2))
+    for i in range(0,le_common):
+        if random.random()>mutation_rate:
+            r_offspring1+=enh_parent2[i]+" "
+            r_offspring2+=enh_parent1[i]+" "
+        else:
+            r_offspring1+=enh_parent1[i]+" "
+            r_offspring2+=enh_parent2[i]+" "
+    if len(enh_parent1)>le_common:
+        for j in range(le_common,len(enh_parent1)):
+            if random.random()>mutation_rate:
+                r_offspring1+=enh_parent1[j]+" "
+            else:   
+                r_offspring2+=enh_parent1[j]+" "
+    if len(enh_parent2)>le_common:
+        for j in range(le_common,len(enh_parent2)):
+            if random.random()>mutation_rate:
+                r_offspring1+=enh_parent2[j]+" "
+            else:
+                r_offspring2+=enh_parent2[j]+" "
+    rules_offspring1['H']=r_offspring1
+    rules_offspring2['H']=r_offspring2
+
+    rules_parent1 = lsystem_parent1.rules["N"].split()
+    rules_parent2 = lsystem_parent2.rules["N"].split()
+    enh_parent1 = []
+    enh_parent2 = []
+    i = 0
+    while i < len(rules_parent1):
+        if rules_parent1[i][:4] in ['addf','addk','addl','addr','addb','addt']:
+            new_token= rules_parent1[i] + " " + rules_parent1[i+1]
+            enh_parent1.append(new_token)
+            i+=1
+        if rules_parent1[i][:4] in ['movf','movk','movl','movr','movb','movt']:
+            enh_parent1.append(rules_parent1[i])
+        if rules_parent1[i]=='N':
+            enh_parent1.append(rules_parent1[i])
+        i+=1 
+    i = 0
+    while i < len(rules_parent2):
+        if rules_parent2[i][:4] in ['addf','addk','addl','addr','addb','addt']:
+            new_token= rules_parent2[i] + " " + rules_parent2[i+1]
+            enh_parent2.append(new_token)
+            i+=1
+        if rules_parent2[i][:4] in ['movf','movk','movl','movr','movb','movt']:
+            enh_parent2.append(rules_parent2[i])
+        if rules_parent2[i]=='N':
+            enh_parent2.append(rules_parent2[i])
+        i+=1 
+    r_offspring1=""
+    r_offspring2=""
+    le_common = min(len(enh_parent1),len(enh_parent2))
+    for i in range(0,le_common):
+        if random.random()>mutation_rate:
+            r_offspring1+=enh_parent2[i]+" "
+            r_offspring2+=enh_parent1[i]+" "
+        else:
+            r_offspring1+=enh_parent1[i]+" "
+            r_offspring2+=enh_parent2[i]+" "
+    if len(enh_parent1)>le_common:
+        for j in range(le_common,len(enh_parent1)):
+            if random.random()>mutation_rate:
+                r_offspring1+=enh_parent1[j]+" "
+            else:   
+                r_offspring2+=enh_parent1[j]+" "
+    if len(enh_parent2)>le_common:
+        for j in range(le_common,len(enh_parent2)):
+            if random.random()>mutation_rate:
+                r_offspring1+=enh_parent2[j]+" "
+            else:
+                r_offspring2+=enh_parent2[j]+" "
+    rules_offspring1['N']=r_offspring1
+    rules_offspring2['N']=r_offspring2
+
+    iter_offspring1+=lsystem_parent2.iterations
+    iter_offspring2+=lsystem_parent1.iterations
+    offspring1=LSystemDecoder(axiom_offspring1,rules_offspring1,iter_offspring1,lsystem_parent1.max_elements,lsystem_parent1.max_depth,lsystem_parent1.verbose)
+    offspring2=LSystemDecoder(axiom_offspring2,rules_offspring2,iter_offspring2,lsystem_parent2.max_elements,lsystem_parent2.max_depth,lsystem_parent2.verbose)
+    return offspring1,offspring2
+
+def initialization_lsystem(max_elements=32,max_depth=8,add_temperature=0.5,none_temperature=0.2,verbose=0):
+    axiom = "C"
+    rules = {}
+    nb_item_C=random.choice(range(6,10))
+    nb_item_H=random.choice(range(2,10))
+    nb_item_B=random.choice(range(2,10))
+    nb_item_N=random.choice(range(2,10))
+    rule_string_C = "C"
+    for i in range(0,nb_item_C):
+        what_to_add = random.choices(['add','mov'],weights=[add_temperature,1-add_temperature])[0]  
+        match what_to_add:
+            case 'add':
+                operator = random.choice(['addf','addk','addl','addr','addb','addt'])
+                rotation = random.choice([0,45,90,135,180,225,270])
+                op_to_add=operator+"("+str(rotation)+")"
+                item_to_add=random.choices(['B','H','N'],weights=[(1-none_temperature)/2,(1-none_temperature)/2,none_temperature])[0]
+                rule_string_C+=" "+op_to_add+" "+item_to_add
+            case 'mov':
+                operator = random.choice(['movf','movk','movl','movr','movb','movt'])
+                rule_string_C+=" "+operator
+    rule_string_B="B"
+    for i in range(0,nb_item_B):
+        what_to_add = random.choice(['add','mov'])
+        match what_to_add:
+            case 'add':
+                operator = random.choice(['addf','addk','addl','addr','addb','addt'])
+                rotation = random.choice([0,45,90,135,180,225,270])
+                op_to_add=operator+"("+str(rotation)+")"
+                item_to_add=random.choices(['B','H','N'],weights=[(1-none_temperature)/2,(1-none_temperature)/2,none_temperature])[0]
+                rule_string_B+=" "+op_to_add+" "+item_to_add
+            case 'mov':
+                operator = random.choice(['movf','movk','movl','movr','movb','movt'])
+                rule_string_B+=" "+operator
+    rule_string_H="H"
+    for i in range(0,nb_item_H):
+        what_to_add = random.choice(['add','mov'])
+        match what_to_add:
+            case 'add':
+                operator = random.choice(['addf','addk','addl','addr','addb','addt'])
+                rotation = random.choice([0,45,90,135,180,225,270])
+                op_to_add=operator+"("+str(rotation)+")"
+                item_to_add=random.choices(['B','H','N'],weights=[(1-none_temperature)/2,(1-none_temperature)/2,none_temperature])[0]
+                rule_string_H+=" "+op_to_add+" "+item_to_add
+            case 'mov':
+                operator = random.choice(['movf','movk','movl','movr','movb','movt'])
+                rule_string_H+=" "+operator
+    rule_string_N="N"
+    for i in range(0,nb_item_H):
+        what_to_add = random.choice(['add','mov'])
+        match what_to_add:
+            case 'add':
+                operator = random.choice(['addf','addk','addl','addr','addb','addt'])
+                rotation = random.choice([0,45,90,135,180,225,270])
+                op_to_add=operator+"("+str(rotation)+")"
+                item_to_add=random.choices(['B','H','N'],weights=[(1-none_temperature)/2,(1-none_temperature)/2,none_temperature])[0]
+                rule_string_N+=" "+op_to_add+" "+item_to_add
+            case 'mov':
+                operator = random.choice(['movf','movk','movl','movr','movb','movt'])
+                rule_string_N+=" "+operator
+    rules['C']=rule_string_C
+    rules['B']=rule_string_B
+    rules['H']=rule_string_H
+    rules['N']=rule_string_N
+    iterations = random.choice(range(2,4))
+    ls = LSystemDecoder(axiom,rules,iterations,max_elements=max_elements,max_depth=max_depth,verbose=verbose)
+    return ls
diff --git a/src/ariel/ec/genotypes/genotype.py b/src/ariel/ec/genotypes/genotype.py
new file mode 100644
index 00000000..4969e0b2
--- /dev/null
+++ b/src/ariel/ec/genotypes/genotype.py
@@ -0,0 +1,49 @@
+from __future__ import annotations
+from abc import ABC, abstractmethod
+from enum import Enum
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from ariel.ec.mutations import Mutation
+    from ariel.ec.crossovers import Crossover
+import networkx as nx
+
+class Genotype(ABC):
+    """Interface for different genotype types."""
+
+    @staticmethod
+    @abstractmethod
+    def get_crossover_object() -> "Crossover":
+        """Return the crossover operator for this genotype type."""
+        raise NotImplementedError("Crossover operator not implemented for this genotype type.")
+    
+    @staticmethod
+    @abstractmethod
+    def get_mutator_object() -> "Mutation":
+        """Return the mutator operator for this genotype type."""
+        raise NotImplementedError("Mutator operator not implemented for this genotype type.")
+    
+    @staticmethod
+    @abstractmethod
+    def create_individual(**kwargs: dict) -> "Genotype":
+        """Generate a new individual of this genotype type."""
+        raise NotImplementedError("Individual generation not implemented for this genotype type.")
+
+    @staticmethod
+    @abstractmethod
+    def to_digraph(robot_genotype: "Genotype", **kwargs: dict) -> nx.DiGraph:
+        """Convert the genotype to a directed graph representation."""
+        raise NotImplementedError("Conversion to directed graph not implemented for this genotype type.")
+    
+    @staticmethod
+    @abstractmethod
+    def to_json(robot_genotype: "Genotype", **kwargs: dict) -> str:
+        """Convert the genotype to a JSON-serializable dictionary."""
+        raise NotImplementedError("Conversion to JSON not implemented for this genotype type.")
+    
+    @staticmethod
+    @abstractmethod
+    def from_json(json_data: str, **kwargs: dict) -> "Genotype":
+        """Create a genotype instance from a JSON-serializable dictionary."""
+        raise NotImplementedError("Creation from JSON not implemented for this genotype type.")
+    
\ No newline at end of file
diff --git a/src/ariel/ec/genotypes/genotype_mapping.py b/src/ariel/ec/genotypes/genotype_mapping.py
new file mode 100644
index 00000000..4d401175
--- /dev/null
+++ b/src/ariel/ec/genotypes/genotype_mapping.py
@@ -0,0 +1,7 @@
+from ariel.ec.genotypes.tree.tree_genome import TreeGenome
+from ariel.ec.genotypes.lsystem.l_system_genotype import LSystemDecoder
+
+GENOTYPES_MAPPING = {
+    'tree': TreeGenome,
+    'lsystem': LSystemDecoder,
+}
\ No newline at end of file
diff --git a/src/ariel/ec/genotypes/integers/integers_genome.py b/src/ariel/ec/genotypes/integers/integers_genome.py
new file mode 100644
index 00000000..4a06b1bc
--- /dev/null
+++ b/src/ariel/ec/genotypes/integers/integers_genome.py
@@ -0,0 +1,64 @@
+from collections.abc import Sequence
+from typing import cast
+import numpy as np
+from ariel.ec.genotypes.genotype import Genotype
+from ariel.ec.mutations import IntegerMutator
+from ariel.ec.crossovers import IntegerCrossover
+from pydantic import BaseSettings
+
+SEED = 42
+RNG = np.random.default_rng(SEED)
+# Type Aliases
+type Integers = Sequence[int]
+type Floats = Sequence[float]
+
+class IntegersGenome(Genotype):
+
+    @staticmethod
+    def get_crossover_object() -> "IntegerCrossover":
+        return IntegerCrossover()
+    
+    @staticmethod
+    def get_mutator_object() -> "IntegerMutator":
+        return IntegerMutator()
+
+    @staticmethod
+    def create_individual(
+        low: int,
+        high: int,
+        size: int | Sequence[int] | None = 1,
+        *,
+        endpoint: bool | None = None,
+    ) -> Integers:
+        endpoint = endpoint
+        generated_values = RNG.integers(
+            low=low,
+            high=high,
+            size=size,
+            endpoint=endpoint,
+        )
+        return cast("Integers", generated_values.astype(int).tolist())
+
+    @staticmethod
+    def choice(
+        value_set: int | Integers,
+        size: int | Sequence[int] | None = 1,
+        probabilities: Sequence[float] | None = None,
+        axis: int = 0,
+        *,
+        replace: bool | None = None,
+        shuffle: bool | None = None,
+    ) -> Integers:
+        replace = replace
+        shuffle = shuffle
+        generated_values = np.array(
+            RNG.choice(
+                a=value_set,
+                size=size,
+                replace=replace,
+                p=probabilities,
+                axis=axis,
+                shuffle=shuffle,
+            ),
+        )
+        return cast("Integers", generated_values.astype(int).tolist())
diff --git a/src/ariel/ec/genotypes/lsystem/__init__.py b/src/ariel/ec/genotypes/lsystem/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/src/ariel/ec/genotypes/lsystem/l_system_genotype.py b/src/ariel/ec/genotypes/lsystem/l_system_genotype.py
new file mode 100644
index 00000000..37b0aa33
--- /dev/null
+++ b/src/ariel/ec/genotypes/lsystem/l_system_genotype.py
@@ -0,0 +1,777 @@
+"""Example of L-system-based decoding for modular robot graphs.
+
+Author:     omn
+Date:       2025-09-26
+Py Ver:     3.12
+OS:         macOS Tahoe 26
+Status:     Prototype
+
+Notes
+-----
+    * This decoder uses an L-system string as the genotype to generate a directed graph (DiGraph) using NetworkX.
+    * The L-system rules and axiom define the growth of the modular robot structure.
+
+References
+----------
+    [1] https://en.wikipedia.org/wiki/L-system
+    [2] https://networkx.org/documentation/stable/reference/readwrite/generated/networkx.readwrite.json_graph.tree_data.html
+
+"""
+
+# Standard library
+from __future__ import annotations
+import json
+import re
+from pathlib import Path
+from typing import Any, Callable, Dict, Optional, TYPE_CHECKING
+from enum import Enum
+
+# Third-party libraries
+import matplotlib.pyplot as plt
+import networkx as nx
+from networkx import DiGraph
+from networkx.readwrite import json_graph
+
+
+# Local libraries
+from ariel.body_phenotypes.robogen_lite.config import ModuleFaces, ModuleRotationsTheta, ModuleType, ModuleInstance,ModuleRotationsIdx
+
+from ariel.ec.genotypes.genotype import Genotype
+if TYPE_CHECKING:
+    from ariel.ec.mutations import LSystemMutator
+    from ariel.ec.crossovers import LSystemCrossover
+
+SEED = 42
+DPI = 300
+
+class SymbolToModuleType(Enum): # for auto-transcoding between L-system string characters and ModuleType elements
+    """Enum for module types."""
+
+    C = 'CORE'
+    B = 'BRICK'
+    H = 'HINGE'
+    N = 'NONE'
+
+class lsystem_element:
+    def __init__(self):
+        self.rotation=0
+        self.front = None
+        self.back = None
+        self.right = None
+        self.left = None
+        self.top = None
+        self.bottom=None
+        self.allowed_connection=['TOP','BOTTOM','LEFT','RIGHT','FRONT','BACK']
+        self.name=''
+
+    def connect_to(self,side,obj,rotation):
+        if side in obj.allowed_connection:
+            match side:
+                case 'TOP':
+                    if obj.top == None:
+                        self.back=obj
+                        self.rotation=rotation
+                        self.back.top=self
+                case 'BOTTOM':
+                    if obj.bottom == None:
+                        self.back=obj
+                        self.rotation=rotation
+                        self.back.bottom=self
+                case 'LEFT':
+                    if obj.left == None:
+                        self.back=obj
+                        self.rotation=rotation
+                        self.back.left=self
+                case 'RIGHT':
+                    if obj.right == None:
+                        self.back=obj
+                        self.rotation=rotation
+                        self.back.right=self
+                case 'FRONT':
+                    if obj.front == None:
+                        self.back=obj
+                        self.rotation=rotation
+                        self.back.front=self
+                case 'BACK':
+                    if obj.back == None:
+                        self.back=obj
+                        self.rotation=rotation
+                        self.back.back=self
+
+    def has_element(self,side):
+        has_element=False
+        match side:
+            case 'TOP':
+                if self.top!=None:
+                    has_element=True
+            case 'BOTTOM':
+                if self.bottom!=None:
+                    has_element=True
+            case 'LEFT':
+                if self.left!=None:
+                    has_element=True
+            case 'RIGHT':
+                if self.right!=None:
+                    has_element=True
+            case 'FRONT':
+                if self.front!=None:
+                    has_element=True
+            case 'BACK':
+                if self.back!=None:
+                    has_element=True
+        return has_element
+
+    def is_allowed(self,side):
+        is_allowed=False
+        if side in self.allowed_connection:
+            is_allowed=True
+        return is_allowed
+    
+    def should_i_explore(self,side):
+        res=False
+        if self.is_allowed(side)==True:
+            if self.has_element(side)==False:
+                res = False
+            else:
+                res = True 
+        else:
+            res = False
+        return res
+
+
+class lsystem_block(lsystem_element):
+
+    def __init__(self):
+        self.rotation=0
+        self.front = None
+        self.back = None
+        self.left = None
+        self.right = None
+        self.top = None
+        self.bottom = None
+        self.allowed_connection=['TOP','BOTTOM','LEFT','RIGHT','FRONT','BACK']
+        self.name='B'
+
+class lsystem_hinge(lsystem_element):
+
+    def __init__(self):
+        self.rotation=0
+        self.front = None
+        self.back = None
+        self.allowed_connection=['FRONT','BACK']
+        self.name='H'
+
+class lsystem_none(lsystem_element):
+
+    def __init__(self):
+        self.rotation=0
+        self.back = None
+        self.allowed_connection=['BACK']
+        self.name='N'
+
+class lsystem_core(lsystem_element):
+
+    def __init__(self):
+        self.rotation=0
+        self.front = None
+        self.back = None
+        self.left = None
+        self.right = None
+        self.allowed_connection=['LEFT','RIGHT','FRONT','BACK']
+        self.name='C'
+
+class LSystemDecoder(Genotype):
+    """Implements an L-system-based decoder for modular robot graphs."""
+
+    def __init__(
+        self,
+        axiom: str,
+        rules: Dict[str, str],
+        iterations: int = 2,
+        max_elements: int = 32,
+        max_depth: int = 8,
+        verbose: int = 0
+    ) -> None:
+        """
+        Initialize the L-system decoder.
+        Automatically expands the L-system and builds the graph.
+        """
+        self.axiom = axiom
+        self.rules = rules
+        self.iterations = iterations
+        self.graph = nx.DiGraph()
+        self.expanded_token=None
+        self.structure = None
+        self.max_elements = max_elements
+        self.max_depth = max_depth
+        self.verbose=verbose
+
+    @staticmethod
+    def get_crossover_object() -> LSystemCrossover:
+        from ariel.ec.crossovers import LSystemCrossover
+        return LSystemCrossover()
+    
+    @staticmethod
+    def get_mutator_object() -> LSystemMutator:
+        from ariel.ec.mutations import LSystemMutator
+        return LSystemMutator()
+    
+    @staticmethod
+    def create_individual(
+        iterations: int = 2,
+        max_elements: int = 32,
+        max_depth: int = 8,
+        verbose: int = 0,
+        **kwargs: dict
+    ) -> LSystemDecoder:
+        base_rules = {"C": "C", "B": "B", "H": "H", "N": "N"}
+
+        indiv = LSystemDecoder(
+            axiom="C",
+            rules=base_rules,
+            iterations=iterations,
+            max_elements=max_elements,
+            max_depth=max_depth,
+            verbose=verbose,
+        )
+        # Materialize expanded_token, structure, and graph
+        indiv.refresh()
+        return indiv
+    
+    @staticmethod
+    def to_json(robot_genotype: LSystemDecoder, *, indent: int = 2) -> str:
+        return json.dumps({
+            "axiom": robot_genotype.axiom,
+            "rules": robot_genotype.rules,
+            "iterations": robot_genotype.iterations,
+            "max_elements": robot_genotype.max_elements,
+            "max_depth": robot_genotype.max_depth,
+            "verbose": robot_genotype.verbose,
+        }, indent=indent)
+    
+    @staticmethod
+    def from_json(json_data: str) -> LSystemDecoder:
+        data = json.loads(json_data)
+        indiv = LSystemDecoder(
+            axiom=data["axiom"],
+            rules=data["rules"],
+            iterations=data["iterations"],
+            max_elements=data["max_elements"],
+            max_depth=data["max_depth"],
+            verbose=data.get("verbose", 0),
+        )
+        # Materialize expanded_token, structure, and graph
+        indiv.refresh()
+        return indiv
+
+    @staticmethod
+    def to_digraph(robot: LSystemDecoder):
+        robot.generate_lsystem_graph()
+        return robot.graph
+
+    def expand_lsystem(self):
+        expanded_token = []
+        expanded_token.append(self.axiom)
+        it = 0
+        while it<self.iterations:
+            for tk in range(0,len(expanded_token)):
+                if expanded_token[tk] in self.rules:
+                    splitted_token = self.rules[expanded_token[tk]].split()
+                    expanded_token[tk]=splitted_token[0]
+                    j=1
+                    while j<len(splitted_token):
+                        expanded_token.insert(tk+j,splitted_token[j])
+                        j+=1
+                    tk+=j
+            it+=1
+        self.expanded_token=expanded_token
+
+    def get_rotation(self,cmd):
+        start = cmd.find("(") + 1
+        end = cmd.find(")")
+        rotation = cmd[start:end]
+        return int(rotation)
+
+
+    def build_lsystem_structure(self):
+        if self.verbose==1: 
+            print("Building L-system structure...")
+        if self.expanded_token!=None and self.expanded_token[0]=='C':
+            self.structure=lsystem_core()
+            turtle = self.structure
+            tk=1
+            add_elements=1
+            while tk < len(self.expanded_token) and add_elements<self.max_elements:
+                if self.verbose==1:
+                    print("token : ",tk,"element count : ",add_elements)
+                match self.expanded_token[tk][:4]:
+                    case 'addf':
+                        if self.verbose==1:
+                            print("add FRONT")
+                        if turtle.is_allowed('FRONT')==False:
+                            if self.verbose==1:
+                                print("ERROR, FRONT connection not allowed, ignored")
+                            tk+=1
+                        else:
+                            if turtle.has_element('FRONT')==True:
+                                if self.verbose==1:
+                                    print("ERROR, FRONT element already exists, ignored")
+                                tk+=1
+                            else:
+                                rotation = self.get_rotation(self.expanded_token[tk])
+                                if tk+1<len(self.expanded_token):
+                                    if self.expanded_token[tk+1] in ['H','B','N']:
+                                        tk+=1
+                                        match self.expanded_token[tk]:
+                                            case 'H':
+                                                if self.verbose==1:
+                                                    print(" with ",rotation,"degree a HINGE")
+                                                new_hinge = lsystem_hinge()
+                                                new_hinge.connect_to('FRONT',turtle, rotation)
+                                            case 'B':
+                                                if self.verbose==1:
+                                                    print(" with ",rotation,"degree a BLOCK")
+                                                new_block = lsystem_block()
+                                                new_block.connect_to('FRONT',turtle, rotation)
+                                            case 'N':
+                                                if self.verbose==1:
+                                                    print(" with ",rotation,"degree a NONE")
+                                                new_none = lsystem_none()
+                                                new_none.connect_to('FRONT',turtle, rotation)
+                                        add_elements+=1
+                                    else:
+                                        if self.verbose==1:
+                                            print("ERROR, unsupported token following ADD, ignored")
+                                else:
+                                    if self.verbose==1:
+                                        print("ERROR, no token following ADD, ignored")
+                    case 'addk':
+                        if self.verbose==1:
+                            print("add BACK")
+                        if turtle.name!="C":    
+                            if self.verbose==1:
+                                print("ERROR, BACK can only be added to CORE elements, ignored") 
+                            tk+=1
+                        else:   
+                            if turtle.has_element('BACK')==True: 
+                                if self.verbose==1:
+                                    print("ERROR, BACK element already exists, ignored")
+                                tk+=1
+                            else:
+                                rotation = self.get_rotation(self.expanded_token[tk])
+                                if tk+1<len(self.expanded_token):
+                                    if self.expanded_token[tk+1] in ['H','B','N']:
+                                        tk+=1
+                                        match self.expanded_token[tk]:
+                                            case 'H':
+                                                if self.verbose==1:
+                                                    print(" with ",rotation,"degree a HINGE")
+                                                new_hinge = lsystem_hinge()
+                                                new_hinge.connect_to('BACK',turtle, rotation)
+                                            case 'B':
+                                                if self.verbose==1:
+                                                    print(" with ",rotation,"degree a BLOCK")
+                                                new_block = lsystem_block()
+                                                new_block.connect_to('BACK',turtle, rotation)
+                                            case 'N':
+                                                if self.verbose==1:
+                                                    print(" with ",rotation,"degree a NONE")
+                                                new_none = lsystem_none()
+                                                new_none.connect_to('BACK',turtle, rotation)
+                                        add_elements+=1
+                                    else:
+                                        if self.verbose==1:
+                                            print("ERROR, unsupported token following ADD, ignored")
+                                else:
+                                    if self.verbose==1:
+                                        print("ERROR, no token following ADD, ignored")
+                    case 'addl':
+                        if self.verbose==1:
+                            print("add LEFT")
+                        if turtle.is_allowed('LEFT')==False:
+                            if self.verbose==1:
+                                print("ERROR, LEFT connection not allowed, ignored")
+                            tk+=1
+                        else:
+                            if turtle.has_element('LEFT')==True: 
+                                if self.verbose==1:
+                                    print("ERROR, LEFT element already exists, ignored")
+                                tk+=1
+                            else:
+                                rotation = self.get_rotation(self.expanded_token[tk])
+                                if tk+1<len(self.expanded_token):
+                                    if self.expanded_token[tk+1] in ['H','B','N']:
+                                        tk+=1
+                                        match self.expanded_token[tk]:
+                                            case 'H':
+                                                if self.verbose==1:
+                                                    print(" with ",rotation,"degree a HINGE")
+                                                new_hinge = lsystem_hinge()
+                                                new_hinge.connect_to('LEFT',turtle, rotation)
+                                            case 'B':
+                                                if self.verbose==1:
+                                                    print(" with ",rotation,"degree a BLOCK")
+                                                new_block = lsystem_block()
+                                                new_block.connect_to('LEFT',turtle, rotation)
+                                            case 'N':
+                                                if self.verbose==1:
+                                                    print(" with ",rotation,"degree a NONE")
+                                                new_none = lsystem_none()
+                                                new_none.connect_to('LEFT',turtle, rotation)
+                                        add_elements+=1
+                                    else:
+                                        if self.verbose==1:
+                                            print("ERROR, unsupported token following ADD, ignored")
+                                else:
+                                    if self.verbose==1:
+                                        print("ERROR, no token following ADD, ignored") 
+                    case 'addr':
+                        if self.verbose==1:
+                            print("add RIGHT")
+                        if turtle.is_allowed('RIGHT')==False:
+                            if self.verbose==1:
+                                print("ERROR, RIGHT connection not allowed, ignored")
+                            tk+=1
+                        else:
+                            if turtle.has_element('RIGHT')==True: 
+                                if self.verbose==1:
+                                    print("ERROR, RIGHT element already exists, ignored")
+                                tk+=1
+                            else:   
+                                rotation = self.get_rotation(self.expanded_token[tk])
+                                if tk+1<len(self.expanded_token):
+                                    if self.expanded_token[tk+1] in ['H','B','N']:
+                                        tk+=1
+                                        match self.expanded_token[tk]:
+                                            case 'H':
+                                                if self.verbose==1:
+                                                    print(" with ",rotation,"degree a HINGE")
+                                                new_hinge = lsystem_hinge()
+                                                new_hinge.connect_to('RIGHT',turtle, rotation)
+                                            case 'B':
+                                                if self.verbose==1:
+                                                    print(" with ",rotation,"degree a BLOCK")
+                                                new_block = lsystem_block()
+                                                new_block.connect_to('RIGHT',turtle, rotation)
+                                            case 'N':
+                                                if self.verbose==1:
+                                                    print(" with ",rotation,"degree a NONE")
+                                                new_none = lsystem_none()
+                                                new_none.connect_to('RIGHT',turtle, rotation)
+                                        add_elements+=1
+                                    else:
+                                        if self.verbose==1:
+                                            print("ERROR, unsupported token following ADD, ignored")
+                                else:
+                                    if self.verbose==1:
+                                        print("ERROR, no token following ADD, ignored")
+                    case 'addt':
+                        if self.verbose==1:
+                            print("add TOP")
+                        if turtle.is_allowed('TOP')==False:
+                            if self.verbose==1:
+                                print("ERROR, TOP connection not allowed, ignored")
+                            tk+=1
+                        else:
+                            if turtle.has_element('TOP')==True: 
+                                if self.verbose==1:
+                                    print("ERROR, TOP element already exists, ignored")
+                                tk+=1
+                            else:   
+                                rotation = self.get_rotation(self.expanded_token[tk])
+                                if tk+1<len(self.expanded_token):
+                                    if self.expanded_token[tk+1] in ['H','B','N']:
+                                        tk+=1
+                                        match self.expanded_token[tk]:
+                                            case 'H':
+                                                if self.verbose==1:
+                                                    print(" with ",rotation,"degree a HINGE")
+                                                new_hinge = lsystem_hinge()
+                                                new_hinge.connect_to('TOP',turtle, rotation)
+                                            case 'B':
+                                                if self.verbose==1:
+                                                    print(" with ",rotation,"degree a BLOCK")
+                                                new_block = lsystem_block()
+                                                new_block.connect_to('TOP',turtle, rotation)
+                                            case 'N':
+                                                if self.verbose==1:
+                                                    print(" with ",rotation,"degree a NONE")
+                                                new_none = lsystem_none()
+                                                new_none.connect_to('TOP',turtle, rotation)
+                                        add_elements+=1
+                                    else:
+                                        if self.verbose==1:
+                                            print("ERROR, unsupported token following ADD, ignored")
+                                else:
+                                    if self.verbose==1:
+                                        print("ERROR, no token following ADD, ignored")
+                    case 'addb':
+                        if self.verbose==1:
+                            print("add BOTTOM")
+                        if turtle.is_allowed('BOTTOM')==False:
+                            if self.verbose==1:
+                                print("ERROR, BOTTOM connection not allowed, ignored")
+                            tk+=1
+                        else:
+                            if turtle.has_element('BOTTOM')==True: 
+                                if self.verbose==1:
+                                    print("ERROR, BOTTOM element already exists, ignored")
+                                tk+=1
+                            else:   
+                                rotation = self.get_rotation(self.expanded_token[tk])
+                                if tk+1<len(self.expanded_token):
+                                    if self.expanded_token[tk+1] in ['H','B','N']:
+                                        tk+=1
+                                        match self.expanded_token[tk]:
+                                            case 'H':
+                                                if self.verbose==1:
+                                                    print(" with ",rotation,"degree a HINGE")
+                                                new_hinge = lsystem_hinge()
+                                                new_hinge.connect_to('BOTTOM',turtle, rotation)
+                                            case 'B':
+                                                if self.verbose==1:
+                                                    print(" with ",rotation,"degree a BLOCK")
+                                                new_block = lsystem_block()
+                                                new_block.connect_to('BOTTOM',turtle, rotation)
+                                            case 'N':
+                                                if self.verbose==1:
+                                                    print(" with ",rotation,"degree a NONE")
+                                                new_none = lsystem_none()
+                                                new_none.connect_to('BOTTOM',turtle, rotation)
+                                        add_elements+=1
+                                    else:
+                                        if self.verbose==1:
+                                            print("ERROR, unsupported token following ADD, ignored")
+                                else:
+                                    if self.verbose==1:
+                                        print("ERROR, no token following ADD, ignored")
+                    case 'movf':
+                        if turtle.should_i_explore('FRONT')==True:
+                            if self.verbose==1:
+                                print("move FRONT")
+                            turtle=turtle.front
+                        else:
+                            if self.verbose==1:
+                                print("ERROR - Cannot move FRONT, ignored")
+                    case 'movk':
+                        if turtle.should_i_explore('BACK')==True:
+                            if self.verbose==1:
+                                print("move BACK")
+                            turtle=turtle.back
+                        else:
+                            if self.verbose==1:
+                                print("ERROR - Cannot move BACK, ignored")
+                    case 'movl':
+                        if turtle.should_i_explore('LEFT')==True:
+                            if self.verbose==1:
+                                print("move LEFT")
+                            turtle=turtle.left
+                        else:
+                            if self.verbose==1:
+                                print("ERROR - Cannot move LEFT, ignored")
+                    case 'movr':
+                        if turtle.should_i_explore('RIGHT')==True:
+                            if self.verbose==1:
+                                print("move RIGHT")
+                            turtle=turtle.right
+                        else:
+                            if self.verbose==1:
+                                print("ERROR - Cannot move RIGHT, ignored")
+                    case 'movt':
+                        if turtle.should_i_explore('TOP')==True:
+                            if self.verbose==1:
+                                print("move TOP")
+                            turtle=turtle.top
+                        else:
+                            if self.verbose==1:
+                                print("ERROR - Cannot move TOP, ignored")
+                    case 'movb':
+                        if turtle.should_i_explore('BOTTOM')==True:
+                            if self.verbose==1:
+                                print("move BOTTOM")
+                            turtle=turtle.bottom
+                        else:
+                            if self.verbose==1:
+                                print("ERROR - Cannot move BOTTOM, ignored")
+                    case _:
+                        if self.verbose==1:
+                            print("ERROR - Unknown command, ignored")
+                tk+=1
+
+    def print_lsystem_element(self,element,id,depth):
+        if depth>self.max_depth or id>self.max_elements:
+            return id
+        connection_map = []
+        for j in ['FRONT','BACK','RIGHT','LEFT','TOP','BOTTOM']:
+            connection_map.append(element.has_element(j))
+        print(element.name,"-",id," - ",connection_map)
+        id_tmp = id
+        if element.should_i_explore('FRONT')==True:
+            print(element.name,"-",id," links FRONT / ",element.front.rotation," degree to ",element.front.name,"-",id_tmp+1)
+            id_tmp=self.print_lsystem_element(element.front,id_tmp+1,depth+1)
+        if element.name=="C":
+            if element.should_i_explore('BACK')==True:
+                print(element.name,"-",id," links BACK / ",element.back.rotation," degree to ",element.back.name,"-",id_tmp+1)
+                id_tmp=self.print_lsystem_element(element.back,id_tmp+1,depth+1)
+        if element.should_i_explore('LEFT')==True:
+            print(element.name,"-",id," links LEFT / ",element.left.rotation," degree to ",element.left.name,"-",id_tmp+1)
+            id_tmp=self.print_lsystem_element(element.left,id_tmp+1,depth+1)
+        if element.should_i_explore('RIGHT')==True:
+            print(element.name,"-",id," links RIGHT / ",element.right.rotation," degree to ",element.right.name,"-",id_tmp+1)
+            id_tmp=self.print_lsystem_element(element.right,id_tmp+1,depth+1)
+        if element.should_i_explore('TOP')==True:
+            print(element.name,"-",id," links TOP / ",element.top.rotation," degree to ",element.top.name,"-",id_tmp+1)
+            id_tmp=self.print_lsystem_element(element.top,id_tmp+1,depth+1)
+        if element.should_i_explore('BOTTOM')==True:
+            print(element.name,"-",id," links BOTTOM / ",element.bottom.rotation," degree to ",element.bottom.name,"-",id_tmp+1)
+            id_tmp=self.print_lsystem_element(element.bottom,id_tmp+1,depth+1)
+        return id_tmp
+
+
+    def print_lsystem_structure(self):
+        self.print_lsystem_element(self.structure,0,0)
+
+    def generate_lsystem_graph_element(self,element,id,depth):
+        if depth>self.max_depth or id>=self.max_elements:
+            return id
+        if id==0:
+            self.graph.add_node(id,type=ModuleType.CORE.name,rotation='DEG_0')
+        id_tmp = id
+        if element.should_i_explore('FRONT')==True:
+            eltype = ModuleType.NONE.name
+            match element.front.name:
+                case 'B':
+                    eltype = ModuleType.BRICK.name
+                case 'H':
+                    eltype = ModuleType.HINGE.name
+            rotation="DEG_"+str(element.front.rotation)
+            self.graph.add_node(id_tmp+1,type=eltype,rotation=rotation)
+            self.graph.add_edge(id,id_tmp+1,face='FRONT')
+            id_tmp=self.generate_lsystem_graph_element(element.front,id_tmp+1,depth+1)
+        if element.name=="C":
+            if element.should_i_explore('BACK')==True:
+                eltype = ModuleType.NONE.name
+                match element.back.name:
+                    case 'B':
+                        eltype = ModuleType.BRICK.name      
+                    case 'H':
+                        eltype = ModuleType.HINGE.name
+                rotation="DEG_"+str(element.back.rotation)
+                self.graph.add_node(id_tmp+1,type=eltype,rotation=rotation)
+                self.graph.add_edge(id,id_tmp+1,face='BACK')
+                id_tmp=self.generate_lsystem_graph_element(element.back,id_tmp+1,depth+1)
+        if element.should_i_explore('RIGHT')==True:
+            eltype = ModuleType.NONE.name
+            match element.right.name:
+                case 'B':
+                    eltype = ModuleType.BRICK.name
+                case 'H':
+                    eltype = ModuleType.HINGE.name
+            rotation="DEG_"+str(element.right.rotation) 
+            self.graph.add_node(id_tmp+1,type=eltype,rotation=rotation)
+            self.graph.add_edge(id,id_tmp+1,face='RIGHT')
+            id_tmp=self.generate_lsystem_graph_element(element.right,id_tmp+1,depth+1)
+        if element.should_i_explore('LEFT')==True:
+            eltype = ModuleType.NONE.name
+            match element.left.name:
+                case 'B':
+                    eltype = ModuleType.BRICK.name
+                case 'H':
+                    eltype = ModuleType.HINGE.name
+            rotation="DEG_"+str(element.left.rotation)
+            self.graph.add_node(id_tmp+1,type=eltype,rotation=rotation)
+            self.graph.add_edge(id,id_tmp+1,face='LEFT')
+            id_tmp=self.generate_lsystem_graph_element(element.left,id_tmp+1,depth+1)
+        if element.should_i_explore('TOP')==True:
+            eltype = ModuleType.NONE.name
+            match element.top.name:
+                case 'B':
+                    eltype = ModuleType.BRICK.name  
+                case 'H':
+                    eltype = ModuleType.HINGE.name
+            rotation="DEG_"+str(element.top.rotation)
+            self.graph.add_node(id_tmp+1,type=eltype,rotation=rotation)
+            self.graph.add_edge(id,id_tmp+1,face='TOP')
+            id_tmp=self.generate_lsystem_graph_element(element.top,id_tmp+1,depth+1)
+        if element.should_i_explore('BOTTOM')==True:
+            eltype = ModuleType.NONE.name
+            match element.bottom.name:
+                case 'B':
+                    eltype = ModuleType.BRICK.name
+                case 'H':
+                    eltype = ModuleType.HINGE.name
+            rotation="DEG_"+str(element.bottom.rotation)
+            self.graph.add_node(id_tmp+1,type=eltype,rotation=rotation)
+            self.graph.add_edge(id,id_tmp+1,face='BOTTOM')
+            id_tmp=self.generate_lsystem_graph_element(element.bottom,id_tmp+1,depth+1)
+        return id_tmp
+
+
+    def generate_lsystem_graph(self):
+        self.graph = nx.DiGraph()
+        self.generate_lsystem_graph_element(self.structure,0,0)
+
+    def print_lsystem_expanded(self):
+        print(self.expanded_token)
+
+    def save_graph_as_json(self,save_file):
+        if save_file is None:
+            return
+        data = json_graph.node_link_data(self.graph, edges="edges")
+        json_string = json.dumps(data, indent=4)
+        with Path(save_file).open("w", encoding="utf-8") as f:
+            f.write(json_string)
+
+    def refresh(self):
+        if self.verbose==1:
+            print("Refreshing L-system decoding...")
+        self.expand_lsystem()
+        self.build_lsystem_structure()
+        self.generate_lsystem_graph()
+
+    def draw_graph(self, title = "L-System Decoded Graph",save_file = None):
+        """Draw the decoded graph using matplotlib and networkx."""
+        plt.figure()
+        pos = nx.spring_layout(self.graph, seed=SEED)
+        options = {
+            "with_labels": True,
+            "node_size": 200,
+            "node_color": "#FFFFFF00",
+            "edgecolors": "blue",
+            "font_size": 8,
+            "width": 0.5,
+        }
+        nx.draw(
+            self.graph,
+            pos,
+            with_labels=True,
+            node_size=150,
+            node_color="#FFFFFF00",
+            edgecolors="blue",
+            font_size=8,
+            width=0.5,
+        )
+        edge_labels = nx.get_edge_attributes(self.graph, "face")
+        nx.draw_networkx_edge_labels(
+            self.graph,
+            pos,
+            edge_labels=edge_labels,
+            font_color="red",
+            font_size=8,
+        )
+        plt.title(title)
+        if save_file!=None:
+            plt.savefig(save_file, dpi=DPI)
+        else:
+            plt.show()
+
+def load_graph_from_json(load_file):
+    with Path(load_file).open("r", encoding="utf-8") as f:
+        data = json.load(f)
+    return json_graph.node_link_graph(
+        data,
+        directed=True,
+        multigraph=False,
+        edges="edges",
+    )
+
diff --git a/src/ariel/ec/genotypes/tree/__init__.py b/src/ariel/ec/genotypes/tree/__init__.py
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/src/ariel/ec/genotypes/tree/__init__.py
@@ -0,0 +1 @@
+
diff --git a/src/ariel/ec/genotypes/tree/tree_genome.py b/src/ariel/ec/genotypes/tree/tree_genome.py
new file mode 100644
index 00000000..7c1802ac
--- /dev/null
+++ b/src/ariel/ec/genotypes/tree/tree_genome.py
@@ -0,0 +1,642 @@
+from __future__ import annotations
+import json
+import ariel.body_phenotypes.robogen_lite.config as config
+import contextlib
+from collections import deque
+import networkx as nx
+from collections.abc import Callable
+from functools import reduce
+import numpy as np
+from typing import TYPE_CHECKING
+
+from ariel.ec.genotypes.genotype import Genotype
+if TYPE_CHECKING:
+    from ariel.ec.crossovers import TreeCrossover
+    from ariel.ec.mutations import TreeMutator
+
+SEED = 42
+RNG = np.random.default_rng(SEED)
+
+class TreeGenome(Genotype):
+    def __init__(self, root: TreeNode | None = None):
+        self._root = root
+
+    @staticmethod
+    def get_crossover_object() -> TreeCrossover:
+        from ariel.ec.crossovers import TreeCrossover
+        """Return the crossover operator for tree genomes."""
+        return TreeCrossover()
+    
+    @staticmethod
+    def get_mutator_object() -> TreeMutator:
+        from ariel.ec.mutations import TreeMutator
+        """Return the mutator operator for tree genomes."""
+        return TreeMutator()
+    
+    @staticmethod
+    def create_individual(**kwargs: dict) -> TreeGenome:
+        """Generate a new TreeGenome individual."""
+        return TreeGenome.default_init()
+    
+    @staticmethod
+    def to_digraph(robot_genotype: TreeGenome, use_node_ids: bool = False) -> nx.DiGraph:
+        g = nx.DiGraph()
+        root = robot_genotype.root
+        if root is None:
+            return g
+
+        # Stable mapping: either identity (node.id) or contiguous DFS ids.
+        if use_node_ids:
+            node_key = lambda n: n.id
+        else:
+            # Assign 0..N-1 in first-seen (DFS) order
+            seen: dict[int, int] = {}
+            next_id = 0
+            def node_key(n: TreeNode) -> int:
+                nonlocal next_id
+                if n.id not in seen:
+                    seen[n.id] = next_id
+                    next_id += 1
+                return seen[n.id]
+
+        def dfs(parent: TreeNode | None, child: TreeNode, via_face: config.ModuleFaces | None) -> None:
+            cid = node_key(child)
+            # Add/update child node with attributes (use enum names for JSON-friendliness)
+            g.add_node(
+                cid,
+                type=child.module_type.name,
+                rotation=child.rotation.name,
+                raw_id=child.id,
+            )
+
+            if parent is not None:
+                pid = node_key(parent)
+                g.add_edge(pid, cid, face=via_face.name if via_face is not None else None)
+
+            # Recurse over children (face -> subnode)
+            for face, sub in child.children.items():
+                dfs(child, sub, face)
+
+        dfs(None, root, None)
+        return g
+
+    @classmethod
+    def default_init(cls, *args, **kwargs):
+        """Default instantiation with a core root."""
+        return cls(root=TreeNode(config.ModuleInstance(type=config.ModuleType.CORE,
+                                              rotation=config.ModuleRotationsIdx.DEG_0,
+                                              links={})))
+
+    @property
+    def root(self) -> TreeNode | None:
+        return self._root
+
+    @root.setter
+    def root(self, value: TreeNode | None):
+        if self._root is not None:
+            raise ValueError("Root node cannot be changed once set.")
+        self._root = value
+
+    @root.getter
+    def root(self) -> TreeNode | None:
+        return self._root
+
+    def __repr__(self) -> str:
+        """Return a nice string representation of the tree genome."""
+        if not self._root:
+            return "TreeGenome(empty)"
+
+        node_count = len(list(self._iter_nodes()))
+        lines = [f"TreeGenome({node_count} nodes):"]
+        lines.extend(self._format_node(self._root, "", True))
+        return "\n".join(lines)
+
+    def _iter_nodes(self):
+        """Iterator over all nodes in the genome."""
+        if self._root:
+            yield from self._iter_nodes_recursive(self._root)
+
+    def _iter_nodes_recursive(self, node: TreeNode):
+        """Recursively iterate over nodes."""
+        yield node
+        for child in node.children.values():
+            yield from self._iter_nodes_recursive(child)
+
+    def _format_node(self, node: TreeNode, prefix: str, is_last: bool) -> list[str]:
+        """Helper method to format a node and its children recursively."""
+        connector = "└── " if is_last else "├── "
+        node_info = f"{node.module_type.name}({node.rotation.name}, depth={node._depth})"
+        lines = [f"{prefix}{connector}{node_info}"]
+
+        child_prefix = prefix + ("    " if is_last else "│   ")
+
+        if node.children:
+            child_items = list(node.children.items())
+            for i, (face, child) in enumerate(child_items):
+                is_last_child = (i == len(child_items) - 1)
+                face_connector = "└── " if is_last_child else "├── "
+                lines.append(f"{child_prefix}{face_connector}[{face.name}]")
+
+                grandchild_prefix = child_prefix + ("    " if is_last_child else "│   ")
+                lines.extend(self._format_node(child, grandchild_prefix, True))
+
+        return lines
+
+    def add_child_to_node(self, node: TreeNode, face: config.ModuleFaces, child_module: config.ModuleInstance):
+        """Helper method to add a child to a specific node. However, not recommended to use. Rather use """
+        if face not in node.available_faces():
+            raise ValueError(f"Face {face} is not available on this node.")
+
+        child_node = TreeNode(child_module, depth=node._depth + 1)
+        setattr(node, face.name.lower(), child_node)
+
+    def find_node(self, target_id: int, method: str = "dfs") -> TreeNode | None:
+        """Find a node by ID in the entire genome."""
+        if not self._root:
+            return None
+
+        if method.lower() == "bfs":
+            return self._root.find_node_bfs(target_id)
+        else:
+            return self._root.find_node_dfs(target_id)
+
+    def find_nodes_by_type(self, module_type: config.ModuleType, method: str = "dfs") -> list[TreeNode]:
+        """Find all nodes of a specific module type."""
+        if not self._root:
+            return []
+
+        predicate = lambda node: node.module_type == module_type
+
+        if method.lower() == "bfs":
+            return self._root.find_all_nodes_bfs(predicate)
+        else:
+            return self._root.find_all_nodes_dfs(predicate)
+
+    def copy(self) -> 'TreeGenome':
+        """Create a shallow copy of the TreeGenome."""
+        new_genome = TreeGenome()
+        if self._root:
+            new_genome._root = self._root.copy()
+        return new_genome
+
+    def __copy__(self) -> 'TreeGenome':
+        """Support for copy.copy()."""
+        return self.copy()
+    
+    # ---------- JSON serialization ----------
+
+    @staticmethod
+    def from_dict(data: dict) -> "TreeGenome":
+        """Deserialize a genome from a dict produced by to_dict()."""
+        root_data = data.get("root")
+        root = None if root_data is None else TreeNode.from_dict(root_data)
+        return TreeGenome(root=root)
+
+    @staticmethod
+    def from_json(json_str: str, **kwargs) -> "TreeGenome":
+        """Deserialize from a JSON string."""
+        return TreeGenome.from_dict(json.loads(json_str))
+    
+    @staticmethod
+    def to_dict(robot_genome: "TreeGenome") -> dict:
+        """Serialize the genome into a pure-Python dict (JSON-friendly)."""
+        return {
+            "root": None if robot_genome._root is None else robot_genome._root.to_dict()
+        }
+
+    @staticmethod
+    def to_json(robot_genome: "TreeGenome", *, indent: int | None = 2) -> str:
+        """Serialize to a JSON string."""
+        return json.dumps(TreeGenome.to_dict(robot_genome), indent=indent)
+
+    # TODO: Implement this
+    # def __deepcopy__(self, memo) -> 'TreeGenome':
+    #     """Support for copy.deepcopy()."""
+    #     new_genome = TreeGenome()
+    #     if self._root:
+    #         new_genome._root = copy.deepcopy(self._root, memo)
+    #     return new_genome
+
+
+class TreeNode:
+
+    def __init__(self, module: config.ModuleInstance | None = None, depth: int = 0,
+                 module_type: config.ModuleType | None = None, module_rotation: config.ModuleRotationsIdx | None = None):
+        if module is None:
+            assert module_type is not None, "Module type cannot be None if module is not specified"
+            assert module_rotation is not None, "Module rotation cannot be None if module is not specified"
+            module = config.ModuleInstance(type=module_type, rotation=module_rotation, links={})
+        self.module_type = module.type
+        self.rotation = module.rotation
+        # Keep reference to the original module. Why? Because then the links get automatically filled and we can just read them out when decoding
+        self.module = module
+        self._depth = depth
+        self._front: TreeNode | None = None
+        self._back: TreeNode | None = None
+        self._right: TreeNode | None = None
+        self._left: TreeNode | None = None
+        self._top: TreeNode | None = None
+        self._bottom: TreeNode | None = None
+
+        self._enable_replacement: bool = False
+
+        self._id = id(self)# if node_id is None else node_id
+
+    @property
+    def id(self) -> int:
+        return self._id
+
+    @id.setter
+    def id(self, value: int | None):
+        raise ValueError("ID cannot be changed once set.")
+    
+    @classmethod
+    def random_tree_node(cls, max_depth: int = 1, branch_prob: float = 0.5) -> 'TreeNode' | None:
+        """Create a random tree node with random children up to max_depth."""
+        if max_depth < 0:
+            return None
+
+        # Exclude CORE and NONE from random selection
+        module_type = RNG.choice([mt for mt in config.ModuleType if mt not in {config.ModuleType.CORE, config.ModuleType.NONE}])
+        module_rotation = RNG.choice(list(config.ModuleRotationsIdx))
+        node = cls(module_type=module_type, module_rotation=module_rotation)
+
+        if max_depth == 0:
+            return node
+
+        available_faces = config.ALLOWED_FACES[module_type]
+        num_children = RNG.integers(0, len(available_faces) + 1)
+        chosen_faces = RNG.choice(available_faces, size=num_children, replace=False)
+
+        for face in chosen_faces:
+            if RNG.random() > branch_prob:
+                continue  # Skip adding a child based on branch probability
+            # Recursively create child nodes with reduced depth
+            child_node = cls.random_tree_node(max_depth - 1)
+            if child_node is not None:
+                node._set_face(face, child_node)
+
+        return node
+
+    @contextlib.contextmanager
+    def enable_replacement(self):
+        """Context manager to temporarily allow replacement of existing children."""
+        all_nodes_to_enable = list(self.get_all_nodes(mode="dfs", exclude_root=False))
+        try:
+            for n in all_nodes_to_enable:
+                n._enable_replacement = True
+            yield
+        finally:
+            for n in all_nodes_to_enable:
+                n._enable_replacement = False
+
+    def _can_attach_to_face(self, face: config.ModuleFaces, node: TreeNode | None) -> bool:
+        """Check if a node can be attached to the given face."""
+        if node is None:
+            return True  # Can always detach (set to None)
+        if face not in config.ALLOWED_FACES[self.module_type]:
+            return False
+        # Check if face is already occupied (unless replacement is enabled)
+        if not self._enable_replacement:
+            face_attr = face.name.lower()
+            if getattr(self, f"_{face_attr}") is not None:
+                return False  # Face already occupied
+        return True
+
+    def _set_face(self, face: config.ModuleFaces, value: 'TreeNode | TreeGenome | None'):
+        """Common method to validate and set a face attribute."""
+        # Handle TreeGenome by extracting its root
+        if isinstance(value, TreeGenome):
+            if value.root is None:
+                raise ValueError("Cannot attach empty TreeGenome (root is None)")
+            actual_value = value.root
+        else:
+            actual_value = value
+
+        if not self._can_attach_to_face(face, actual_value):
+            if actual_value is not None and getattr(self, f"_{face.name.lower()}") is not None:
+                raise ValueError(f"{face.name} face already occupied on {self.module_type}")
+            raise ValueError(f"Cannot attach to {face.name} face of {self.module_type}")
+
+        # Update the internal attribute with the actual node
+        setattr(self, f"_{face.name.lower()}", actual_value)
+
+        # Update the module's links dictionary
+        if actual_value is not None:
+            self.module.links[face] = self._id
+        else:
+            self.module.links.pop(face, None)
+
+    def _get_face_given_child(self, child_id: int) -> config.ModuleFaces | None:
+        # Weird flex
+        # ids_to_faces = reduce(lambda acc, x: {**acc, **x}, map(lambda face_node: {face_node[1].id: face_node[0]}, self.children.items()), {})
+        return {face_node[1].id: face_node[0] for face_node in self.children.items()}[child_id]
+
+    def face_mapping(self, face: config.ModuleFaces):
+        mapping = {
+            config.ModuleFaces.FRONT: self._front,
+            config.ModuleFaces.BACK: self._back,
+            config.ModuleFaces.RIGHT: self._right,
+            config.ModuleFaces.LEFT: self._left,
+            config.ModuleFaces.TOP: self._top,
+            config.ModuleFaces.BOTTOM: self._bottom,
+        }
+        return mapping[face]
+
+    @property
+    def front(self) -> TreeNode | None:
+        return self._front
+
+    @front.setter
+    def front(self, value: 'TreeNode | TreeGenome | None'):
+        self._set_face(config.ModuleFaces.FRONT, value)
+
+    @property
+    def back(self) -> TreeNode | None:
+        return self._back
+
+    @back.setter
+    def back(self, value: 'TreeNode | TreeGenome | None'):
+        self._set_face(config.ModuleFaces.BACK, value)
+
+    @property
+    def right(self) -> TreeNode | None:
+        return self._right
+
+    @right.setter
+    def right(self, value: 'TreeNode | TreeGenome | None'):
+        self._set_face(config.ModuleFaces.RIGHT, value)
+
+    @property
+    def left(self) -> TreeNode | None:
+        return self._left
+
+    @left.setter
+    def left(self, value: 'TreeNode | TreeGenome | None'):
+        self._set_face(config.ModuleFaces.LEFT, value)
+
+    @property
+    def top(self) -> TreeNode | None:
+        return self._top
+
+    @top.setter
+    def top(self, value: 'TreeNode | TreeGenome | None'):
+        self._set_face(config.ModuleFaces.TOP, value)
+
+    @property
+    def bottom(self) -> TreeNode | None:
+        return self._bottom
+
+    @bottom.setter
+    def bottom(self, value: 'TreeNode | TreeGenome | None'):
+        self._set_face(config.ModuleFaces.BOTTOM, value)
+
+    @property
+    def children(self) -> dict[config.ModuleFaces, TreeNode]:
+        result = {}
+        for face in config.ALLOWED_FACES[self.module_type]:
+            child = self.face_mapping(face)
+            if child is not None:
+                result[face] = child
+        return result
+
+    def __eq__(self, other: object) -> bool:
+        """Two nodes are equal if they have the same ID."""
+        # This is my approach now (Lukas), we could also check for other equalities.
+        if not isinstance(other, TreeNode):
+            return False
+        return self._id == other._id
+
+    def __hash__(self) -> int:
+        """Make TreeNode hashable using its ID."""
+        return hash(self._id)
+
+    def __ne__(self, other: object) -> bool:
+        """Not equal is the opposite of equal."""
+        return not self.__eq__(other)
+
+    def available_faces(self) -> list[config.ModuleFaces]:
+        """Return list of faces that can still accept children."""
+        available = []
+        for face in config.ALLOWED_FACES[self.module_type]:
+            if self.face_mapping(face) is None:
+                available.append(face)
+        return available
+
+    def __repr__(self) -> str:
+        """Return a nice string representation of the tree node."""
+        child_count = len(self.children)
+        available_count = len(self.available_faces())
+        child_info = f", {child_count} children" if child_count > 0 else ""
+        available_info = f", {available_count} available faces" if available_count > 0 else ""
+        return f"TreeNode({self.module_type.name}, {self.rotation.name}, depth={self._depth}{child_info}{available_info})"
+
+    def add_child(self, face: config.ModuleFaces, child_module: config.ModuleInstance):
+        """Add a child to the specified face."""
+        if face not in self.available_faces():
+            raise ValueError(f"Face {face} is not available for attachment.")
+
+        child_node = TreeNode(child_module, depth=self._depth + 1)
+        setattr(self, face.name.lower(), child_node)
+
+    def remove_child(self, face: config.ModuleFaces):
+        """Remove a child from the specified face."""
+        if face not in config.ALLOWED_FACES[self.module_type]:
+            raise ValueError(f"Face {face} is not valid for module type {self.module_type}.")
+
+        setattr(self, face.name.lower(), None)
+
+    def get_child(self, face: config.ModuleFaces) -> 'TreeNode | None':
+        """Get the child at the specified face."""
+        if face not in config.ALLOWED_FACES[self.module_type]:
+            return None
+        return getattr(self, face.name.lower(), None)
+
+    def find_node_dfs(self, target_id: int) -> 'TreeNode | None':
+        """Find a node by ID using Depth-First Search."""
+        if self._id == target_id:
+            return self
+
+        # Search children recursively
+        for child in self.children.values():
+            result = child.find_node_dfs(target_id)
+            if result is not None:
+                return result
+
+        return None
+
+    def find_node_bfs(self, target_id: int) -> 'TreeNode | None':
+        """Find a node by ID using Breadth-First Search."""
+        queue = deque([self])
+
+        while queue:
+            current = queue.popleft()
+
+            if current._id == target_id:
+                return current
+
+            # Add all children to queue
+            queue.extend(current.children.values())
+
+        return None
+
+    def find_all_nodes_dfs(self, predicate: Callable[TreeNode, bool] | None = None) -> list['TreeNode']:
+        """Find all nodes matching a predicate using DFS."""
+        result = []
+
+        def dfs_helper(node: 'TreeNode'):
+            if predicate is None or predicate(node):
+                result.append(node)
+
+            for child in node.children.values():
+                dfs_helper(child)
+
+        dfs_helper(self)
+        return result
+
+    def find_all_nodes_bfs(self, predicate: Callable[TreeNode, bool] = None) -> list['TreeNode']:
+        """Find all nodes matching a predicate using BFS."""
+        result = []
+        queue = deque([self])
+
+        while queue:
+            current = queue.popleft()
+
+            if predicate is None or predicate(current):
+                result.append(current)
+
+            queue.extend(current.children.values())
+
+        return result
+
+    def get_all_nodes(self, mode: str = "dfs", exclude_root: bool = True):
+        """
+        Returns all the nodes in the subtree that has self as root node
+        """
+        predicate_root: Callable[TreeNode, bool] = (lambda x: x.id != self.id) if exclude_root else (lambda _: True)
+        if mode == "dfs":
+            return self.find_all_nodes_dfs(predicate=predicate_root)
+        elif mode == "bfs":
+            return self.find_all_nodes_bfs(predicate=predicate_root)
+        else:
+            raise ValueError("Invalid mode. Valid modes: dfs, bfs")
+
+    def get_internal_nodes(self, mode: str = "dfs", exclude_root: bool = True):
+        """
+        Returns all the non-leaf nodes in the subtree that has self as root node
+        """
+        predicate_root: Callable[TreeNode, bool] = (lambda x: x.id != self.id) if exclude_root else (lambda _: True)
+        predicate_internal_nodes: Callable[TreeNode, bool] = (lambda x: len(x.children) > 0)
+        predicate_list = [predicate_root, predicate_internal_nodes]
+        predicate: Callable[TreeNode, bool] = lambda x: reduce(lambda p, q: p(x) and q(x), predicate_list)
+        if mode == "dfs":
+            return self.find_all_nodes_dfs(predicate=predicate)
+        elif mode == "bfs":
+            return self.find_all_nodes_bfs(predicate=predicate)
+        else:
+            raise ValueError("Invalid mode. Valid modes: dfs, bfs")
+
+    def replace_node(self, node_to_remove: TreeNode, node_to_add: TreeNode):
+        """
+        1) Finds the parent of node_to_remove in self subtree
+        2) Replaces node_to_remove with node_to_add in the parent's children
+        3)
+        """
+        predicate_is_parent = lambda x: node_to_remove in set(x.children.values())
+        parent = self.find_all_nodes_dfs(predicate=predicate_is_parent)
+        # print("PARENTS LENGTH",len(parent))
+        if not parent or len(parent) > 1:
+            raise RuntimeError("Father not found, are you sure node_to_remove is in subtree?")
+        # We expect a list of len 1 in which there is the parent
+        parent = parent[0]
+        parent._set_face(parent._get_face_given_child(node_to_remove.id), node_to_add)
+
+    def copy(self) -> 'TreeNode':
+        """Create a deep copy of this node and all its children."""
+        # Create new module instance
+        new_module = config.ModuleInstance(
+            type=self.module_type,
+            rotation=self.rotation,
+            links={}  # Will be rebuilt
+        )
+
+        # Create new node
+        new_node = TreeNode(new_module, depth=self._depth,)
+
+        # Recursively copy children
+        for face, child in self.children.items():
+            child_copy = child.copy()
+            new_node._set_face(face, child_copy)
+
+        return new_node
+
+    def __copy__(self) -> 'TreeNode':
+        """Support for copy.copy() - creates deep copy for tree structures."""
+        return self.copy()
+    
+    # ---------- JSON / dict serialization ----------
+    def to_dict(self) -> dict:
+        """
+        Serialize this node recursively.
+        Enums are stored by name (string). Children are a mapping face->child.
+        """
+        # Children as {face_name: child_dict}
+        children_dict = {
+            face.name: child.to_dict()
+            for face, child in self.children.items()
+        }
+        return {
+            "id": self._id,
+            "depth": self._depth,
+            "module_type": self.module_type.name,
+            "rotation": self.rotation.name,
+            "children": children_dict,
+        }
+
+    @classmethod
+    def from_dict(cls, data: dict) -> "TreeNode":
+        """
+        Rebuild a TreeNode (and its subtree) from dict.
+        Uses _set_face so that ModuleInstance.links remains consistent.
+        """
+        #node_id = data["id"]
+        depth = data["depth"]
+        module_type = config.ModuleType[data["module_type"]]
+        rotation = config.ModuleRotationsIdx[data["rotation"]]
+
+        # Create the node with a fresh ModuleInstance and the preserved id/depth
+        node = cls(
+            module=config.ModuleInstance(type=module_type, rotation=rotation, links={}),
+            depth=depth,
+            #node_id=node_id,
+        )
+
+        # Rebuild children through _set_face so links are updated properly.
+        children = data.get("children", {}) or {}
+        for face_name, child_payload in children.items():
+            face = config.ModuleFaces[face_name]
+            child_node = cls.from_dict(child_payload)
+            node._set_face(face, child_node)
+
+        return node
+
+
+
+def test():
+    genome = TreeGenome()
+    genome.root = TreeNode(config.ModuleInstance(type=config.ModuleType.BRICK, rotation=config.ModuleRotationsIdx.DEG_90, links={}))
+    genome.root.front = TreeNode(config.ModuleInstance(type=config.ModuleType.BRICK, rotation=config.ModuleRotationsIdx.DEG_45, links={}))
+    genome.root.left = TreeNode(config.ModuleInstance(type=config.ModuleType.BRICK, rotation=config.ModuleRotationsIdx.DEG_45, links={}))
+    #genome.root.front = TreeNode(config.ModuleInstance(type=config.ModuleType.HINGE, rotation=config.ModuleRotationsIdx.DEG_90, links={}))
+    with genome.root.enable_replacement():
+        genome.root.front = TreeNode(config.ModuleInstance(type=config.ModuleType.HINGE, rotation=config.ModuleRotationsIdx.DEG_45, links={}))
+    print(genome.root.front.available_faces())
+    print(genome)  # Shows full tree structure
+    print(genome.root)  # Shows node details with available faces
+    with genome.root.enable_replacement():
+        genome.root.replace_node(genome.root.front, genome.root.left)
+    print(genome.root.get_all_nodes("dfs", True))
+
+
+if __name__ == "__main__":
+    test()
\ No newline at end of file
diff --git a/src/ariel/ec/mutations.py b/src/ariel/ec/mutations.py
new file mode 100644
index 00000000..7a3339ba
--- /dev/null
+++ b/src/ariel/ec/mutations.py
@@ -0,0 +1,378 @@
+"""TODO(jmdm): description of script."""
+from __future__ import annotations
+# Standard library
+from abc import ABC, abstractmethod
+from collections.abc import Sequence
+from pathlib import Path
+from typing import cast, TYPE_CHECKING, List
+import random
+
+# Third-party libraries
+import numpy as np
+from pydantic_settings import BaseSettings
+from rich.console import Console
+from rich.traceback import install
+import copy
+if TYPE_CHECKING:
+    from ariel.ec.genotypes.genotype import Genotype
+import ariel.body_phenotypes.robogen_lite.config as pheno_config
+
+# Global constants
+SCRIPT_NAME = __file__.split("/")[-1][:-3]
+CWD = Path.cwd()
+DATA = CWD / "__data__"
+DATA.mkdir(exist_ok=True)
+DB_NAME = "database.db"
+DB_PATH: Path = DATA / DB_NAME
+SEED = 42
+
+# Global functions
+install(width=180)
+console = Console()
+RNG = np.random.default_rng(SEED)
+
+class Mutation(ABC):
+    mutations_mapping: dict[str, function] = NotImplemented
+    which_mutation: str = ""
+
+    @classmethod
+    def set_which_mutation(cls, mutation_type: str) -> None:
+        cls.which_mutation = mutation_type
+
+    def __init_subclass__(cls):
+        super().__init_subclass__()
+        cls.mutations_mapping = {
+            name: getattr(cls, name)
+            for name, val in cls.__dict__.items()
+            if isinstance(val, staticmethod)
+        }
+
+    @classmethod
+    def __call__(
+        cls,
+        individual: Genotype,
+        **kwargs: dict,
+    ) -> Genotype:
+        """Perform crossover on two genotypes.
+
+        Parameters
+        ----------
+        parent_i : Genotype
+            The first parent genotype (list or nested list of integers).
+        parent_j : Genotype
+            The second parent genotype (list or nested list of integers).
+
+        Returns
+        -------
+        tuple[Genotype, Genotype]
+            Two child genotypes resulting from the crossover.
+        """
+        if cls.which_mutation in cls.mutations_mapping:
+            return cls.mutations_mapping[cls.which_mutation](
+                individual,
+                **kwargs
+            )
+        else:
+            msg = f"Mutation type '{cls.which_mutation}' not recognized."
+            raise ValueError(msg)
+
+class IntegerMutator(Mutation):
+
+    @staticmethod
+    def random_swap(
+        individual: Genotype,
+        low: int,
+        high: int,
+        mutation_probability: float,
+    ) -> Genotype:
+        shape = np.asarray(individual).shape
+        mutator = RNG.integers(
+            low=low,
+            high=high,
+            size=shape,
+            endpoint=True,
+        )
+        mask = RNG.choice(
+            [True, False],
+            size=shape,
+            p=[mutation_probability, 1 - mutation_probability],
+        )
+        new_genotype = np.where(mask, mutator, individual).astype(int).tolist()
+        return cast("Integers", new_genotype.astype(int).tolist())
+
+    @staticmethod
+    def integer_creep(
+        individual: Genotype,
+        span: int,
+        mutation_probability: float,
+    ) -> Genotype:
+        # Prep
+        ind_arr = np.array(individual)
+        shape = ind_arr.shape
+
+        # Generate mutation values
+        mutator = RNG.integers(
+            low=1,
+            high=span,
+            size=shape,
+            endpoint=True,
+        )
+
+        # Include negative mutations
+        sub_mask = RNG.choice(
+            [-1, 1],
+            size=shape,
+        )
+
+        # Determine which positions to mutate
+        do_mask = RNG.choice(
+            [1, 0],
+            size=shape,
+            p=[mutation_probability, 1 - mutation_probability],
+        )
+        mutation_mask = mutator * sub_mask * do_mask
+        new_genotype = ind_arr + mutation_mask
+        return cast("Integers", new_genotype.astype(int).tolist())
+
+
+# class TreeGenerator:
+#     @staticmethod
+#     def __call__(*args, **kwargs) -> TreeGenome:
+#         return TreeGenome.default_init()
+
+#     @staticmethod
+#     def default():
+#         return TreeGenome.default_init()
+
+#     @staticmethod
+#     def linear_chain(length: int = 3) -> TreeGenome:
+#         """Generate a linear chain of modules (snake-like)."""
+#         genome = TreeGenome.default_init()  # Start with CORE
+#         current_node = genome.root
+
+#         for i in range(length):
+#             module_type = RNG.choice([pheno_config.ModuleType.BRICK, pheno_config.ModuleType.HINGE])
+#             rotation = RNG.choice(list(pheno_config.ModuleRotationsIdx))
+#             module = pheno_config.ModuleInstance(type=module_type, rotation=rotation, links={})
+
+#             # Always attach to FRONT face for linear chain
+#             if pheno_config.ModuleFaces.FRONT in current_node.available_faces():
+#                 child = TreeNode(module, depth=current_node._depth + 1)
+#                 current_node._set_face(pheno_config.ModuleFaces.FRONT, child)
+#                 current_node = child
+
+#         return genome
+
+#     @staticmethod
+#     def star_shape(num_arms: int = 3) -> TreeGenome:
+#         """Generate a star-shaped tree with arms radiating from center."""
+#         genome = TreeGenome.default_init()  # Start with CORE
+#         available_faces = genome.root.available_faces()
+
+#         # Limit arms to available faces
+#         actual_arms = min(num_arms, len(available_faces))
+#         selected_faces = RNG.choice(available_faces, size=actual_arms, replace=False)
+
+#         for face in selected_faces:
+#             module_type = RNG.choice([pheno_config.ModuleType.BRICK, pheno_config.ModuleType.HINGE])
+#             rotation = RNG.choice(list(pheno_config.ModuleRotationsIdx))
+#             module = pheno_config.ModuleInstance(type=module_type, rotation=rotation, links={})
+
+#             child = TreeNode(module, depth=1)
+#             genome.root._set_face(face, child)
+
+#         return genome
+
+#     @staticmethod
+#     def binary_tree(depth: int = 2) -> TreeGenome:
+#         """Generate a binary-like tree structure."""
+#         def build_subtree(current_depth: int, max_depth: int) -> TreeNode | None:
+#             if current_depth >= max_depth:
+#                 return None
+
+#             module_type = RNG.choice([pheno_config.ModuleType.BRICK, pheno_config.ModuleType.HINGE])
+#             rotation = RNG.choice(list(pheno_config.ModuleRotationsIdx))
+#             module = pheno_config.ModuleInstance(type=module_type, rotation=rotation, links={})
+
+#             node = TreeNode(module, depth=current_depth)
+#             available_faces = node.available_faces()
+
+#             # Add 1-2 children randomly
+#             if available_faces and current_depth < max_depth - 1:
+#                 num_children = RNG.integers(1, min(3, len(available_faces) + 1))
+#                 selected_faces = RNG.choice(available_faces, size=num_children, replace=False)
+
+#                 for face in selected_faces:
+#                     child = build_subtree(current_depth + 1, max_depth)
+#                     if child:
+#                         node._set_face(face, child)
+
+#             return node
+
+#         genome = TreeGenome.default_init()
+
+#         # Add children to root
+#         available_faces = genome.root.available_faces()
+#         if available_faces:
+#             num_children = RNG.integers(1, min(3, len(available_faces) + 1))
+#             selected_faces = RNG.choice(available_faces, size=num_children, replace=False)
+
+#             for face in selected_faces:
+#                 child = build_subtree(1, depth)
+#                 if child:
+#                     genome.root._set_face(face, child)
+
+#         return genome
+
+#     @staticmethod
+#     def random_tree(max_depth: int = 4, branching_prob: float = 0.7) -> TreeGenome:
+#         """Generate a random tree with pheno_configurable branching probability."""
+#         genome = TreeGenome.default_init()  # Start with CORE
+#         face = RNG.choice(genome.root.available_faces())
+#         subtree = TreeNode.random_tree_node(max_depth=max_depth - 1, branch_prob=branching_prob)
+#         if subtree:
+#             genome.root._set_face(face, subtree)
+#         return genome
+
+class TreeMutator(Mutation):
+
+    @staticmethod
+    def _random_tree(max_depth: int = 2, branching_prob: float = 0.5) -> Genotype:
+        """Generate a random tree with pheno_configurable branching probability."""
+        from ariel.ec.genotypes.tree.tree_genome import TreeGenome, TreeNode
+        genome = TreeGenome.default_init()  # Start with CORE
+        face = RNG.choice(genome.root.available_faces())
+        subtree = TreeNode.random_tree_node(max_depth=max_depth - 1, branch_prob=branching_prob)
+        if subtree:
+            genome.root._set_face(face, subtree)
+        return genome
+
+    @staticmethod
+    def random_subtree_replacement(
+        individual: Genotype,
+        max_subtree_depth: int = 2,
+        branching_prob: float = 0.5,
+    ) -> Genotype:
+        """Replace a random subtree with a new random subtree."""
+        from ariel.ec.genotypes.tree.tree_genome import TreeNode
+        new_individual = copy.copy(individual)
+
+        # Collect all nodes in the tree
+        all_nodes = new_individual.root.get_all_nodes(exclude_root=True)
+
+        if not all_nodes:
+            # print("Tree has no nodes to replace; generating a new random tree.")
+            return TreeMutator._random_tree(max_depth=max_subtree_depth, branching_prob=branching_prob)
+
+        # Generate a new random subtree
+        new_subtree = TreeNode.random_tree_node(max_depth=max_subtree_depth, branch_prob=branching_prob)
+
+        node_to_replace = RNG.choice(all_nodes)
+
+        with new_individual.root.enable_replacement():
+            new_individual.root.replace_node(node_to_replace, new_subtree)
+
+        return new_individual
+
+class LSystemMutator(Mutation):
+
+    @staticmethod
+    def mutate_one_point_lsystem(lsystem,mutation_rate,add_temperature=0.5):
+        # op_completed = ""
+        if random.random()<mutation_rate:
+            action=random.choices(['add_rule','rm_rule'],weights=[add_temperature,1-add_temperature])[0]
+            rules = lsystem.rules
+            rule_to_change=random.choice(range(0,len(rules)))
+            rl_tmp = list(rules.values())[rule_to_change]
+            splitted_rules=rl_tmp.split()
+            gene_to_change=random.choice(range(0,len(splitted_rules)))
+            match action:
+                case 'add_rule':
+                    operator=random.choice(['addf','addk','addl','addr','addb','addt','movf','movk','movl','movr','movt','movb'])
+                    if operator in ['addf','addk','addl','addr','addb','addt']:
+                        if splitted_rules[gene_to_change][:4] in ['addf','addk','addl','addr','addb','addt']:
+                            rotation = random.choice([0,45,90,135,180,225,270])
+                            op_to_add=operator+"("+str(rotation)+")"
+                            item_to_add=random.choice(['B','H','N'])
+                            splitted_rules.insert(gene_to_change+2,item_to_add)
+                            splitted_rules.insert(gene_to_change+2,op_to_add)
+                            # op_completed="ADDED : "+op_to_add+" "+item_to_add
+                        elif splitted_rules[gene_to_change][:4] in ['movf','movk','movl','movr','movb','movt']:
+                            rotation = random.choice([0,45,90,135,180,225,270])
+                            op_to_add=operator+"("+str(rotation)+")"
+                            item_to_add=random.choice(['B','H','N'])
+                            splitted_rules.insert(gene_to_change,item_to_add)
+                            splitted_rules.insert(gene_to_change,op_to_add)
+                            # op_completed="ADDED : "+op_to_add+" "+item_to_add
+                        elif splitted_rules[gene_to_change] in ['C','B','H','N']:
+                            rotation = random.choice([0,45,90,135,180,225,270])
+                            op_to_add=operator+"("+str(rotation)+")"
+                            item_to_add=random.choice(['B','H','N'])
+                            splitted_rules.insert(gene_to_change+1,item_to_add)
+                            splitted_rules.insert(gene_to_change+1,op_to_add)
+                            # op_completed="ADDED : "+op_to_add+" "+item_to_add
+                    if operator in ['movf','movk','movl','movr','movb','movt']:
+                        if splitted_rules[gene_to_change][:4] in ['addf','addk','addl','addr','addb','addt']:
+                            splitted_rules.insert(gene_to_change+2,operator)
+                            # op_completed="ADDED : "+operator
+                        elif splitted_rules[gene_to_change][:4] in ['movf','movk','movl','movr','movb','movt']:
+                            splitted_rules.insert(gene_to_change,operator)
+                            # op_completed="ADDED : "+operator
+                        elif splitted_rules[gene_to_change] in ['C','B','H','N']:
+                            splitted_rules.insert(gene_to_change+1,operator)
+                            # op_completed="ADDED : "+operator
+                case 'rm_rule':
+                    if splitted_rules[gene_to_change][:4] in ['addf','addk','addl','addr','addb','addt']:
+                        # op_completed="REMOVED : "+splitted_rules[gene_to_change]+" "+splitted_rules[gene_to_change+1]
+                        splitted_rules.pop(gene_to_change)
+                        splitted_rules.pop(gene_to_change)
+                    elif splitted_rules[gene_to_change] in ['H','B','N']:
+                        # op_completed="REMOVED : "+splitted_rules[gene_to_change-1]+" "+splitted_rules[gene_to_change]
+                        if gene_to_change-1>=0:
+                            splitted_rules.pop(gene_to_change-1)
+                            splitted_rules.pop(gene_to_change-1)
+                    elif splitted_rules[gene_to_change][:4] in ['movf','movk','movl','movr','movt','movb']:
+                        # op_completed="REMOVED : "+splitted_rules[gene_to_change]
+                        splitted_rules.pop(gene_to_change)
+            new_rule = ""
+            for j in range(0,len(splitted_rules)):
+                new_rule+=splitted_rules[j]+" "
+            if new_rule!="":
+                lsystem.rules[list(rules.keys())[rule_to_change]]=new_rule
+            else:
+                lsystem.rules[list(rules.keys())[rule_to_change]]=lsystem.rules[list(rules.keys())[rule_to_change]]
+        return lsystem
+
+
+def test() -> None:
+    """Entry point."""
+    console.log(IntegersGenerator.integers(-5, 5, 5))
+    example = IntegersGenerator.choice([1, 3, 4], (2, 5))
+    console.log(example)
+    example2 = IntegerMutator.integer_creep(
+        example,
+        span=1,
+        mutation_probability=1,
+    )
+    console.log(example2)
+
+    console.rule("[bold blue]Tree Generator Examples")
+
+    treeGenerator = TreeGenerator()
+    random_tree = treeGenerator.random_tree(max_depth=3, branching_prob=0.7)
+    console.log("Random Tree:", random_tree)
+
+    genome = TreeGenome()
+    genome.root = TreeNode(pheno_config.ModuleInstance(type=pheno_config.ModuleType.BRICK, rotation=pheno_config.ModuleRotationsIdx.DEG_90, links={}))
+    genome.root.front = TreeNode(pheno_config.ModuleInstance(type=pheno_config.ModuleType.BRICK, rotation=pheno_config.ModuleRotationsIdx.DEG_45, links={}))
+    genome.root.left = TreeNode(pheno_config.ModuleInstance(type=pheno_config.ModuleType.BRICK, rotation=pheno_config.ModuleRotationsIdx.DEG_45, links={}))
+    tree_mutator = TreeMutator()
+    mutated_genome = tree_mutator.random_subtree_replacement(genome, max_subtree_depth=1)
+    console.log("Original Genome:", genome)
+    console.log("Mutated Genome:", mutated_genome)
+
+
+
+
+if __name__ == "__main__":
+    test()
diff --git a/src/ariel/utils/graph_ops.py b/src/ariel/utils/graph_ops.py
new file mode 100644
index 00000000..5b8166b8
--- /dev/null
+++ b/src/ariel/utils/graph_ops.py
@@ -0,0 +1,52 @@
+"""
+Graph operations for robot phenotype manipulation.
+
+Functions for converting between different graph representations.
+"""
+
+import json
+from typing import Union
+import networkx as nx
+
+
+def robot_json_to_digraph(json_data: Union[dict, str]) -> nx.DiGraph:
+    """
+    Convert a robot JSON to a NetworkX directed graph.
+    
+    :param json_data: Robot data as dict or JSON string
+    :returns: NetworkX directed graph representation
+    """
+    if isinstance(json_data, str):
+        robot_data = json.loads(json_data)
+    else:
+        robot_data = json_data
+    
+    graph = nx.DiGraph()
+    
+    # Add nodes with their attributes
+    for node in robot_data.get("nodes", []):
+        node_id = node["id"]
+        graph.add_node(node_id, 
+                      type=node["type"], 
+                      rotation=node["rotation"])
+    
+    # Add edges with face information
+    for edge in robot_data.get("edges", []):
+        graph.add_edge(edge["source"], 
+                      edge["target"], 
+                      face=edge["face"])
+    
+    return graph
+
+
+def load_robot_json_file(file_path: str) -> nx.DiGraph:
+    """
+    Load a robot JSON file and convert it to a NetworkX directed graph.
+    
+    :param file_path: Path to the JSON file
+    :returns: NetworkX directed graph representation
+    """
+    with open(file_path, 'r') as f:
+        robot_data = json.load(f)
+    
+    return robot_json_to_digraph(robot_data)
\ No newline at end of file
diff --git a/src/ariel/utils/morphological_descriptor.py b/src/ariel/utils/morphological_descriptor.py
new file mode 100644
index 00000000..55ef26ac
--- /dev/null
+++ b/src/ariel/utils/morphological_descriptor.py
@@ -0,0 +1,735 @@
+"""
+MorphologicalMeasures class for robot phenotype digraph analysis.
+Mostly based on the revolve implementation: https://github.com/ci-group/revolve2/blob/master/standards/revolve2/standards/morphological_measures.py
+"""
+
+from itertools import product
+from typing import Generic, TypeVar, Any
+
+import numpy as np
+from numpy.typing import NDArray
+import networkx as nx
+
+import ariel.body_phenotypes.robogen_lite.config as config
+
+TModule = TypeVar("TModule", bound=np.generic)
+
+
+class MorphologicalMeasures(Generic[TModule]):
+    """
+    Modular robot morphological measures for robot phenotype digraph.
+
+    Works with a NetworkX directed graph representation of a robot.
+    Only works for robot with only right angle module rotations (90 degrees).
+    Some measures only work for 2d robots, which is noted in their docstring.
+
+    The measures are based on the following paper:
+    Miras, K., Haasdijk, E., Glette, K., Eiben, A.E. (2018).
+    Search Space Analysis of Evolvable Robot Morphologies.
+    In: Sim, K., Kaufmann, P. (eds) Applications of Evolutionary Computation.
+    EvoApplications 2018. Lecture Notes in Computer Science(), vol 10784. Springer, Cham.
+    https://doi.org/10.1007/978-3-319-77538-8_47
+    """
+
+    """Represents the modules of a body in a 3D tensor."""
+    grid: NDArray[TModule]
+    symmetry_grid: NDArray[TModule]
+    """Position of the core in 'body_as_grid'."""
+    core_grid_position: np.ndarray
+
+    """If the robot is two dimensional, i.e. all module rotations are 0 degrees."""
+    is_2d: bool
+
+    """The robot graph structure."""
+    graph: nx.DiGraph
+    core_node: Any
+    modules: list[Any]
+    bricks: list[Any]
+    active_hinges: list[Any]
+
+    """If all slots of the core are filled with other modules."""
+    core_is_filled: bool
+
+    """Bricks which have all slots filled with other modules."""
+    filled_bricks: list[Any]
+
+    """Active hinges which have all slots filled with other modules."""
+    filled_active_hinges: list[Any]
+
+    """
+    Modules that only connect to one other module.
+
+    This includes children and parents.
+    """
+    single_neighbour_modules: list[Any]
+
+    """
+    Bricks that are only connected to one other module.
+
+    Both children and parent are counted.
+    """
+    single_neighbour_bricks: list[Any]
+
+    """
+    Bricks that are connected to exactly two other modules.
+
+    Both children and parent are counted.
+    """
+    double_neighbour_bricks: list[Any]
+
+    """
+    Active hinges that are connected to exactly two other modules.
+
+    Both children and parent are counted.
+    """
+    double_neighbour_active_hinges: list[Any]
+
+    """
+    X/Y-plane symmetry according to the paper but in 3D.
+
+    X-axis is defined as forward/backward for the core module
+    Y-axis is defined as left/right for the core module.
+    """
+    xy_symmetry: float
+
+    """
+    X/Z-plane symmetry according to the paper but in 3D.
+
+    X-axis is defined as forward/backward for the core module
+    Z-axis is defined as up/down for the core module.
+    """
+    xz_symmetry: float
+
+    """
+    Y/Z-plane symmetry according to the paper but in 3D.
+
+    Y-axis is defined as left/right for the core module.
+    Z-axis is defined as up/down for the core module.
+    """
+    yz_symmetry: float
+
+    def __init__(self, robot_graph: nx.DiGraph) -> None:
+        """
+        Initialize this object.
+
+        :param robot_graph: The NetworkX directed graph representing the robot phenotype.
+                           Expected to have node attributes 'type' and 'rotation'.
+                           Expected to have edge attributes 'face'.
+        """
+        if robot_graph.number_of_nodes() == 0:
+            raise ValueError("Cannot analyze empty robot graph")
+
+        self.graph = robot_graph
+        self.grid, self.core_grid_position = self._graph_to_grid(robot_graph)
+        self.core_node = self._find_core_node()
+        self.is_2d = self._calculate_is_2d()
+        self.modules = list(robot_graph.nodes())
+        self.bricks = self._get_nodes_by_type("BRICK")
+        self.active_hinges = self._get_nodes_by_type("HINGE")
+        self.core_is_filled = self._calculate_core_is_filled()
+        self.filled_bricks = self._calculate_filled_bricks()
+        self.filled_active_hinges = self._calculate_filled_active_hinges()
+        self.single_neighbour_bricks = self._calculate_single_neighbour_bricks()
+        self.single_neighbour_modules = self._calculate_single_neighbour_modules()
+        self.double_neighbour_bricks = self._calculate_double_neighbour_bricks()
+        self.double_neighbour_active_hinges = (
+            self._calculate_double_neighbour_active_hinges()
+        )
+
+        self._pad_grid()
+        self.xy_symmetry = self._calculate_xy_symmetry()
+        self.xz_symmetry = self._calculate_xz_symmetry()
+        self.yz_symmetry = self._calculate_yz_symmetry()
+
+    def _find_core_node(self) -> Any:
+        """Find the core node (root of the tree) in the graph."""
+        # Find node with no predecessors (root)
+        roots = [node for node in self.graph.nodes() if self.graph.in_degree(node) == 0]
+        if len(roots) != 1:
+            raise ValueError(f"Expected exactly one root node, found {len(roots)}")
+        return roots[0]
+
+    def _get_nodes_by_type(self, module_type: str) -> list[Any]:
+        """Get all nodes of a specific module type."""
+        return [node for node in self.graph.nodes()
+                if self.graph.nodes[node].get('type') == module_type]
+
+    def _calculate_is_2d(self) -> bool:
+        """Check if all modules use only 90-degree rotations."""
+        valid_rotations = {"DEG_0", "DEG_90", "DEG_180", "DEG_270"}
+        return all(
+            self.graph.nodes[node].get('rotation', 'DEG_0') in valid_rotations
+            for node in self.graph.nodes()
+        )
+
+    def _get_node_type(self, node: Any) -> str:
+        """Get the module type of a node."""
+        return self.graph.nodes[node].get('type', 'UNKNOWN')
+
+    def _get_allowed_faces(self, node: Any) -> list[str]:
+        """Get allowed faces for a node based on its type."""
+        module_type = self._get_node_type(node)
+        if module_type == "CORE":
+            return ["FRONT", "BACK", "RIGHT", "LEFT", "TOP", "BOTTOM"]
+        elif module_type == "BRICK":
+            return ["FRONT", "RIGHT", "LEFT", "TOP", "BOTTOM"]
+        elif module_type == "HINGE":
+            return ["FRONT"]
+        else:
+            return []
+
+    def _get_node_connections(self, node: Any) -> list[str]:
+        """Get the faces that are connected for a node."""
+        connected_faces = []
+        # Check outgoing edges (children)
+        for successor in self.graph.successors(node):
+            edge_data = self.graph.get_edge_data(node, successor)
+            if edge_data and 'face' in edge_data:
+                connected_faces.append(edge_data['face'])
+        return connected_faces
+
+    def _count_neighbors(self, node: Any) -> int:
+        """Count total neighbors (predecessors + successors)."""
+        return self.graph.in_degree(node) + self.graph.out_degree(node)
+
+    def _graph_to_grid(self, robot_graph: nx.DiGraph) -> tuple[NDArray[TModule], np.ndarray]:
+        """Convert robot graph to 3D grid representation."""
+        if robot_graph.number_of_nodes() == 0:
+            raise ValueError("Cannot convert empty robot graph to grid")
+
+        # Calculate positions of all nodes relative to core
+        positions = {}
+        core_node = self._find_core_node()
+        self._calculate_graph_positions(core_node, positions, np.array([0, 0, 0]))
+
+        # Find bounds
+        if not positions:
+            # Single core only
+            positions[core_node] = np.array([0, 0, 0])
+
+        pos_array = np.array(list(positions.values()))
+        min_pos = pos_array.min(axis=0)
+        max_pos = pos_array.max(axis=0)
+
+        # Create grid with proper size
+        grid_size = max_pos - min_pos + 1
+        grid = np.full(grid_size, None, dtype=object)
+
+        # Place nodes in grid
+        core_pos = positions[core_node] - min_pos
+        for node in robot_graph.nodes():
+            node_pos = positions[node] - min_pos
+            grid[tuple(node_pos)] = node
+
+        return grid, core_pos
+
+    def _calculate_graph_positions(self, node: Any, positions: dict, pos: np.ndarray) -> None:
+        """Recursively calculate 3D positions of all nodes in the graph."""
+        positions[node] = pos.copy()
+
+        # Define face direction vectors (assuming standard orientation)
+        face_directions = {
+            "FRONT": np.array([1, 0, 0]),
+            "BACK": np.array([-1, 0, 0]),
+            "RIGHT": np.array([0, 1, 0]),
+            "LEFT": np.array([0, -1, 0]),
+            "TOP": np.array([0, 0, 1]),
+            "BOTTOM": np.array([0, 0, -1]),
+        }
+
+        # Process children (successors in the graph)
+        for child in self.graph.successors(node):
+            edge_data = self.graph.get_edge_data(node, child)
+            if edge_data and 'face' in edge_data:
+                face = edge_data['face']
+                if face in face_directions:
+                    child_pos = pos + face_directions[face]
+                    if child not in positions:  # Avoid cycles
+                        self._calculate_graph_positions(child, positions, child_pos)
+
+    def _calculate_core_is_filled(self) -> bool:
+        """Check if the core has all its allowed faces filled."""
+        allowed_faces = self._get_allowed_faces(self.core_node)
+        connected_faces = self._get_node_connections(self.core_node)
+        return len(connected_faces) == len(allowed_faces)
+
+    def _calculate_filled_bricks(self) -> list[Any]:
+        """Get bricks that have all their allowed faces filled."""
+        return [
+            brick
+            for brick in self.bricks
+            if len(self._get_node_connections(brick)) == len(self._get_allowed_faces(brick))
+        ]
+
+    def _calculate_filled_active_hinges(self) -> list[Any]:
+        """Get active hinges that have all their allowed faces filled."""
+        return [
+            hinge
+            for hinge in self.active_hinges
+            if len(self._get_node_connections(hinge)) == len(self._get_allowed_faces(hinge))
+        ]
+
+    def _calculate_single_neighbour_bricks(self) -> list[Any]:
+        """Get bricks that have no children (leaf nodes)."""
+        return [
+            brick
+            for brick in self.bricks
+            if self.graph.out_degree(brick) == 0
+        ]
+
+    def _calculate_single_neighbour_modules(self) -> list[Any]:
+        """Get non-core modules that have only one neighbor (leaf nodes)."""
+        non_core_modules = [node for node in self.modules if self._get_node_type(node) != "CORE"]
+        return [
+            module
+            for module in non_core_modules
+            if self._count_neighbors(module) == 1
+        ]
+
+    def _calculate_double_neighbour_bricks(self) -> list[Any]:
+        """Get bricks that have exactly one child (connecting two modules)."""
+        return [
+            brick
+            for brick in self.bricks
+            if self.graph.out_degree(brick) == 1
+        ]
+
+    def _calculate_double_neighbour_active_hinges(self) -> list[Any]:
+        """Get active hinges that have exactly one child (connecting two modules)."""
+        return [
+            hinge
+            for hinge in self.active_hinges
+            if self.graph.out_degree(hinge) == 1
+        ]
+
+    def _pad_grid(self) -> None:
+        x, y, z = self.grid.shape
+        xoffs, yoffs, zoffs = self.core_grid_position
+        self.symmetry_grid = np.empty(
+            shape=(x + xoffs, y + yoffs, z + zoffs), dtype=object
+        )
+        self.symmetry_grid.fill(None)
+        self.symmetry_grid[:x, :y, :z] = self.grid
+
+    def _calculate_xy_symmetry(self) -> float:
+        """Calculate XY-plane symmetry."""
+        num_along_plane = 0
+        num_symmetrical = 0
+        for x, y, z in product(
+            range(self.bounding_box_depth),
+            range(self.bounding_box_width),
+            range(1, (self.bounding_box_height - 1) // 2),
+        ):
+            if self.symmetry_grid[x, y, self.core_grid_position[2]] is not None:
+                num_along_plane += 1
+            pos_z = self.symmetry_grid[x, y, self.core_grid_position[2] + z]
+            neg_z = self.symmetry_grid[x, y, self.core_grid_position[2] - z]
+            if pos_z is not None and neg_z is not None:
+                # Check if module types match
+                if self._get_node_type(pos_z) == self._get_node_type(neg_z):
+                    num_symmetrical += 2
+
+        difference = self.num_modules - num_along_plane
+        return num_symmetrical / difference if difference > 0.0 else 0.0
+
+    def _calculate_xz_symmetry(self) -> float:
+        """Calculate XZ-plane symmetry."""
+        num_along_plane = 0
+        num_symmetrical = 0
+        for x, y, z in product(
+            range(self.bounding_box_depth),
+            range(1, (self.bounding_box_width - 1) // 2),
+            range(self.bounding_box_height),
+        ):
+            if self.symmetry_grid[x, self.core_grid_position[1], z] is not None:
+                num_along_plane += 1
+            pos_y = self.symmetry_grid[x, self.core_grid_position[1] + y, z]
+            neg_y = self.symmetry_grid[x, self.core_grid_position[1] - y, z]
+            if pos_y is not None and neg_y is not None:
+                # Check if module types match
+                if self._get_node_type(pos_y) == self._get_node_type(neg_y):
+                    num_symmetrical += 2
+        difference = self.num_modules - num_along_plane
+        return num_symmetrical / difference if difference > 0.0 else 0.0
+
+    def _calculate_yz_symmetry(self) -> float:
+        """Calculate YZ-plane symmetry."""
+        num_along_plane = 0
+        num_symmetrical = 0
+        for x, y, z in product(
+            range(1, (self.bounding_box_depth - 1) // 2),
+            range(self.bounding_box_width),
+            range(self.bounding_box_height),
+        ):
+            if self.symmetry_grid[self.core_grid_position[0], y, z] is not None:
+                num_along_plane += 1
+            pos_x = self.symmetry_grid[self.core_grid_position[0] + x, y, z]
+            neg_x = self.symmetry_grid[self.core_grid_position[0] - x, y, z]
+            if pos_x is not None and neg_x is not None:
+                # Check if module types match
+                if self._get_node_type(pos_x) == self._get_node_type(neg_x):
+                    num_symmetrical += 2
+        difference = self.num_modules - num_along_plane
+        return num_symmetrical / difference if difference > 0.0 else 0.0
+
+    @property
+    def bounding_box_depth(self) -> int:
+        """
+        Get the depth of the bounding box around the body.
+
+        Forward/backward axis for the core module.
+
+        :returns: The depth.
+        """
+        return self.grid.shape[0]
+
+    @property
+    def bounding_box_width(self) -> int:
+        """
+        Get the width of the bounding box around the body.
+
+        Right/left axis for the core module.
+
+        :returns: The width.
+        """
+        return self.grid.shape[1]
+
+    @property
+    def bounding_box_height(self) -> int:
+        """
+        Get the height of the bounding box around the body.
+
+        Up/down axis for the core module.
+
+        :returns: The height.
+        """
+        return self.grid.shape[2]
+
+    @property
+    def num_modules(self) -> int:
+        """
+        Get the number of modules.
+
+        :returns: The number of modules.
+        """
+        return len(self.modules)
+
+    @property
+    def num_bricks(self) -> int:
+        """
+        Get the number of bricks.
+
+        :returns: The number of bricks.
+        """
+        return len(self.bricks)
+
+    @property
+    def num_active_hinges(self) -> int:
+        """
+        Get the number of active hinges.
+
+        :returns: The number of active hinges.
+        """
+        return len(self.active_hinges)
+
+    @property
+    def num_filled_bricks(self) -> int:
+        """
+        Get the number of bricks which have all slots filled with other modules.
+
+        :returns: The number of bricks.
+        """
+        return len(self.filled_bricks)
+
+    @property
+    def num_filled_active_hinges(self) -> int:
+        """
+        Get the number of bricks which have all slots filled with other modules.
+
+        :returns: The number of bricks.
+        """
+        return len(self.filled_active_hinges)
+
+    @property
+    def num_filled_modules(self) -> int:
+        """
+        Get the number of modules which have all slots filled with other modules, including the core.
+
+        :returns: The number of modules.
+        """
+        return (
+            self.num_filled_bricks
+            + self.num_filled_active_hinges
+            + (1 if self.core_is_filled else 0)
+        )
+
+    @property
+    def max_potentionally_filled_core_and_bricks(self) -> int:
+        """
+        Get the maximum number of core and bricks that could potentially be filled with this set of modules if rearranged in an optimal way.
+
+        This calculates 'b_max' from the paper.
+
+        :returns: The calculated number.
+        """
+        pot_max_filled = max(0, (self.num_modules - 2) // 3)
+        pot_max_filled = min(pot_max_filled, 1 + self.num_bricks)
+        return pot_max_filled
+
+    @property
+    def filled_core_and_bricks_proportion(self) -> float:
+        """
+        Get the ratio between filled cores and bricks and how many that potentially could have been if this set of modules was rearranged in an optimal way.
+
+        This calculates 'branching' from the paper.
+
+        :returns: The proportion.
+        """
+        if self.max_potentionally_filled_core_and_bricks == 0:
+            return 0.0
+
+        return (
+            len(self.filled_bricks) + (1 if self.core_is_filled else 0)
+        ) / self.max_potentionally_filled_core_and_bricks
+
+    @property
+    def num_single_neighbour_modules(self) -> int:
+        """
+        Get the number of bricks that are only connected to one other module.
+
+        Both children and parent are counted.
+
+        :returns: The number of bricks.
+        """
+        return len(self.single_neighbour_modules)
+
+    @property
+    def max_potential_single_neighbour_modules(self) -> int:
+        """
+        Get the maximum number of bricks that could potentially have only one neighbour if this set of modules was rearranged in an optimal way.
+
+        This calculates "l_max" from the paper.
+
+        :returns: The calculated number.
+        """
+        return self.num_modules - 1 - max(0, (self.num_modules - 3) // 3)
+
+    @property
+    def num_double_neighbour_bricks(self) -> int:
+        """
+        Get the number of bricks that are connected to exactly two other modules.
+
+        Both children and parent are counted.
+
+        :returns: The number of bricks.
+        """
+        return len(self.double_neighbour_bricks)
+
+    @property
+    def num_double_neighbour_active_hinges(self) -> int:
+        """
+        Get the number of active hinges that are connected to exactly two other modules.
+
+        Both children and parent are counted.
+
+        :returns: The number of active hinges.
+        """
+        return len(self.double_neighbour_active_hinges)
+
+    @property
+    def potential_double_neighbour_bricks_and_active_hinges(self) -> int:
+        """
+        Get the maximum number of bricks and active hinges that could potentially have exactly two neighbours if this set of modules was rearranged in an optimal way.
+
+        This calculates e_max from the paper.
+
+        :returns: The calculated number.
+        """
+        return max(0, self.num_bricks + self.num_active_hinges - 1)
+
+    @property
+    def double_neighbour_brick_and_active_hinge_proportion(self) -> float:
+        """
+        Get the ratio between the number of bricks and active hinges with exactly two neighbours and how many that could potentially have been if this set of modules was rearranged in an optimal way.
+
+        This calculate length of limbs proportion(extensiveness) from the paper.
+
+        :returns: The proportion.
+        """
+        if self.potential_double_neighbour_bricks_and_active_hinges == 0:
+            return 0.0
+
+        return (
+            self.num_double_neighbour_bricks + self.num_double_neighbour_active_hinges
+        ) / self.potential_double_neighbour_bricks_and_active_hinges
+
+    @property
+    def bounding_box_volume(self) -> int:
+        """
+        Get the volume of the bounding box.
+
+        This calculates m_area from the paper.
+
+        :returns: The volume.
+        """
+        return (
+            self.bounding_box_width * self.bounding_box_height * self.bounding_box_depth
+        )
+
+    @property
+    def bounding_box_volume_coverage(self) -> float:
+        """
+        Get the proportion of the bounding box that is filled with modules.
+
+        This calculates 'coverage' from the paper.
+
+        :returns: The proportion.
+        """
+        return self.num_modules / self.bounding_box_volume
+
+    @property
+    def branching(self) -> float:
+        """
+        Get the 'branching' measurement from the paper.
+
+        Alias for filled_core_and_bricks_proportion.
+
+        :returns: Branching measurement.
+        """
+        return self.filled_core_and_bricks_proportion
+
+    @property
+    def limbs(self) -> float:
+        """
+        Get the 'limbs' measurement from the paper.
+
+        Alias for single_neighbour_brick_proportion.
+
+        :returns: Limbs measurement.
+        """
+        if self.max_potential_single_neighbour_modules == 0:
+            return 0.0
+        return (
+            self.num_single_neighbour_modules
+            / self.max_potential_single_neighbour_modules
+        )
+
+    @property
+    def length_of_limbs(self) -> float:
+        """
+        Get the 'length of limbs' measurement from the paper.
+
+        Alias for double_neighbour_brick_and_active_hinge_proportion.
+
+        :returns: Length of limbs measurement.
+        """
+        return self.double_neighbour_brick_and_active_hinge_proportion
+
+    @property
+    def coverage(self) -> float:
+        """
+        Get the 'coverage' measurement from the paper.
+
+        Alias for bounding_box_volume_coverage.
+
+        :returns: Coverage measurement.
+        """
+        return self.bounding_box_volume_coverage
+
+    @property
+    def proportion_2d(self) -> float:
+        """
+        Get the 'proportion' measurement from the paper.
+
+        Only for 2d robots.
+
+        :returns: Proportion measurement.
+        """
+        assert self.is_2d
+
+        return min(self.bounding_box_depth, self.bounding_box_width) / max(
+            self.bounding_box_depth, self.bounding_box_width
+        )
+
+    @property
+    def symmetry(self) -> float:
+        """
+        Get the 'symmetry' measurement from the paper, but extended to 3d.
+
+        :returns: Symmetry measurement.
+        """
+        return max(self.xy_symmetry, self.xz_symmetry, self.yz_symmetry)
+
+    @property
+    def num_joints(self) -> int:
+        """Number of joints (active hinges)."""
+        return self.num_active_hinges
+
+    @property
+    def max_potential_joints(self) -> int:
+        """Maximum possible joints (if every connection were a hinge)."""
+        return max(0, self.num_modules - 1)
+
+    @property
+    def joints(self) -> float:
+        """Get the 'number of joints' measurement J = j / j_max."""
+        if self.max_potential_joints == 0:
+            return 0.0
+        return self.num_joints / self.max_potential_joints
+
+    @property
+    def size(self) -> float:
+    #TODO check if m_max is fine like this!!
+        """Size S = m / m_max (proportion of occupied volume).
+
+        m = number of modules
+        m_max = bounding box volume (max possible occupancy)
+
+        Equivalent to 'coverage' in Miras et al. (2018) if volume is used as reference.
+        """
+        if self.bounding_box_volume == 0:
+            return 0.0
+        return self.num_modules / self.bounding_box_volume
+
+    @property
+    def proportion(self) -> float:
+        """Proportion P = p_s / p_l (only valid for 2D morphologies)."""
+        return self.proportion_2d
+
+    @property
+    def B(self) -> float:
+        """Branching B = b / b_max."""
+        return self.branching
+
+    @property
+    def L(self) -> float:
+        """Length of limbs L = e / e_max."""
+        return self.limbs
+
+    @property
+    def S(self) -> float:
+        """Symmetry S = s."""
+        return self.symmetry
+
+    @property
+    def C(self) -> float:
+        """Coverage C = c."""
+        return self.coverage
+
+    @property
+    def J(self) -> float:
+        """Joints J = j / j_max."""
+        return self.joints
+
+    @property
+    def E(self) -> float:
+        """Extensiveness E = e / e_max."""
+        return self.length_of_limbs
+
+    @property
+    def P(self) -> float:
+        """Proportion P = p_s / p_l (only valid for 2D morphologies)."""
+        if self.is_2d:
+            return self.proportion_2d
+        else:
+            return 0.0  # Return 0 for 3D robots where proportion is not defined
diff --git a/uv.lock b/uv.lock
index e772bceb..b93acba2 100644
--- a/uv.lock
+++ b/uv.lock
@@ -175,6 +175,7 @@ dependencies = [
 
 [package.dev-dependencies]
 dev = [
+    { name = "ipykernel" },
     { name = "opencv-stubs" },
     { name = "types-networkx" },
 ]
@@ -221,6 +222,7 @@ requires-dist = [
 
 [package.metadata.requires-dev]
 dev = [
+    { name = "ipykernel", specifier = ">=6.30.1" },
     { name = "opencv-stubs", specifier = ">=0.1.0" },
     { name = "types-networkx", specifier = ">=3.5.0.20250918" },
 ]