🐏 Tuning improvements #15
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* Add experiment with randomly controlled player * Add EvolutionaryAlgorithm class * Add demo file for EvolutionaryAlgorithm class
…tignore dummy_demo_framework contains an outline & arbitrary classes for the project
* Save progress * Add gitigorne * Code refactor * Final improvments * Add very basic store_data implementation * Save progress * Save progress * Save progress * Minor fixes Co-authored-by: marcgalitski <[email protected]>
* Save progress on Experiment Class * Remove binary and text files * Refactor Experiment class * Save Process * Added Experiment to run_algorithm() * Remove pyc file * Turned on self.logs in environment.py because logs were still being displayed * Made changes to experiment, base_algo, and specialist; added line_plot_method, changed variable name in base_alg, and added hyperparams for specialist * Changed generations_number from 5 to 2 * Changed fitnesses var name to avg_generation_fitness for clarity; added DEBUG statements; requesting clarification for population array * Implemented working line_plot function - plots average generation fitness during each experimental run; TODO: boxplots, fix best_solution array - best does not want to be appended to a list of list... so far solutions get merged into a single list * Base_algo: fixed generation iteratable (increased at beginning & end, instead of just end); experiment.py: fixed calculation of total average - successfully plots total average over all generations in a single experiment against experiment number; specialist: changed some hyperparams Co-authored-by: marcgalitski <[email protected]>
* Average crossover * Add hidden layer to network * Niche fitness refactor
* mutation_ratio to 0.2 * Added uniform_mutation, removed duplicate mutation_ratio (set by specialist as hyperparam) * Added uniform, made changes to attribute names * Changed selection ratio to 0.3
* Add tuning * Fix
* Add tuning * Fix
* Same initial population * Minor improvements
…_framework into tuning-improvements
* Same initial population * Minor improvements * Fix alpha values * Fix
…_framework into tuning-improvements
* Same initial population * Minor improvements * Fix alpha values * Fix * Minor improvement
…_framework into tuning-improvements
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