Genetic-Algorithm-Optimization/main.py at main · ModernityRejecter/Genetic-Algorithm-Optimization · GitHub

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import config
import contextlib
import os
from utils.plotter import generate_plots
from ga.individual import Individual
from ga.population_generator import generate_population
from ga.genetic_algorithm import (
    perform_selection,
    crossover_selection,
    perform_crossover,
    replace_parents,
    perform_mutation,
)
from utils.logger import population_info, show_probabilities, show_intervals, show_crossover_selections, \
    show_crossover_results, show_mutated


os.makedirs("data", exist_ok=True)

def population_stats(population):
    fitness_values = [ind.fitness for ind in population]
    x_values = [ind.x for ind in population]
    f_max = max(fitness_values)
    avg_fitness = sum(fitness_values) / len(fitness_values)
    avg_x = sum(x_values) / len (x_values)
    return f_max, avg_fitness, avg_x


def main():
    population = generate_population()

    max_evolution = []
    avg_evolution = []
    x_evolution = []
    avgx_evolution = []

    with open("data/evolution.txt", "w") as f:
        with contextlib.redirect_stdout(f):

            for generation in range(config.STEPS):

                elite_individual = max(population, key = lambda ind: ind.fitness)
                elite_clone = Individual(elite_individual.chromosome)

                selected_population = perform_selection(population)
                selected_population[0] = elite_clone

                f_max, avg_fitness, avg_x = population_stats(population)

                max_evolution.append(elite_individual.fitness)
                avg_evolution.append(avg_fitness)
                x_evolution.append(elite_individual.x)
                avgx_evolution.append(avg_x)

                crossover_population = crossover_selection(selected_population)
                crossover_results = perform_crossover(crossover_population)
                next_generation = replace_parents(selected_population, crossover_results)
                mutated_population = perform_mutation(next_generation)

                if generation == 0:
                    population_info(population, "Initial population info:")
                    show_probabilities(population)
                    show_intervals(population)


                    population_info(selected_population, "Population after selection:")
                    show_crossover_selections(crossover_population)
                    show_crossover_results(crossover_results, crossover_population)
                    population_info(next_generation, "Next generation info:")
                    show_mutated(mutated_population, next_generation)
                    population_info(mutated_population, "Mutated population info:")


                print(f"Generation {generation + 1}: f_max = {elite_individual.fitness}, avg fitness = {avg_fitness}")

                population = mutated_population

            max_evolution.append(elite_individual.fitness)
            avg_evolution.append(avg_fitness)
            x_evolution.append(elite_individual.x)
            avgx_evolution.append(avg_x)

            print(f"Final Generation {config.STEPS + 1}: f_max = {f_max}, avg fitness = {avg_fitness}")

    generate_plots(max_evolution, avg_evolution, x_evolution, avgx_evolution)

    return population


if __name__ == "__main__":
    final_population = main()