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* The logging in suprb does only work with (at least) numpy 1.22.4 * Mflow is only compatible with older protobuf versions. I only confirmed 3.20.3 to be working
* Add logging of pareto fronts * Add moo sbatch * Change iteration count to 32 and sbatch script * Start runs as a background process * Run datasets as slurm array * Delet study name from sbatch * Remove erroneus" * Add NSGAIII and SPEA2 parameter space * Change sbatch script to support all optimizer * Adding moo plotting * Add sampler experiment * Add missing escape symbol for linbreak * Change experiment naming scheme * Unify run description optimizer names * Add Graph settings for Sampler Comp * Add Projection Comparison * Implement pareto front histograms * Add early stopping tuning * Change max iterations for early stopping to 256 * Add first verison of ES specific plotting * Add iter/hv plots and ga baseline * Change TwoStage comparison test TSComp now only runs two configurations: TS with GA tuning and TS without GA tuning. These will be compared to the MOO baseline. * Add SupTitle to ithv plots * Add Cold staging configs * Fix logging caused by ndarray serialization * Fix logging bug caused by ndarray serialization * Fix early stopping in tscomp * Change es_patience in tscomp * Seed experiments properly * Add TS_ES comparison * Remove unused plots * Fix typo * Fix typo * Add popsize comparison * Add post runtime metrics * Generalize bayesian ttest * Add SOO comparison mode to pf plots * Add confidence ellipses * Restructure runs * Change projection comparison parameter * Change projection comparison parameter * Add 1024 rules run * Remove projection parameter * Fix projection comparison * Fix unused parameter -c in Projection * Change colors and legends on hexbin plots * Add Pareto front test fitness logging * Added MOOCrossValidation to all moo experiments * Fix experiment naming * Implement test_hypervolume based tuning and validation * Adapt moo_experiment.sbatch to only use --config parameters * Add test elitist fitness tuning for soo * Switch GA baseline to test_elitist_fitness tuning * Remove space * Remove averaging of pareto fronts * Update Plotting and add latex tuning tables * Add one_time_run slrum script * Switch to python 3.12 and Fix Plotting * Change look of some plots * Use MOOCV in sampcomp and tscomp * Change plot generation Onetimerun names and Delay * Abgabe * Abgabe * Fix formatting * Upgrade default.nix to 312
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Submission of the evaluation used for my masters thesis, which integrated multi-objective solution composition into SupRB.