deap を利用した OneMax 問題

OneMaxProblem.py

import random
from copy import deepcopy
from deap import base, creator, tools

def eval_one_max(individual) -> tuple:
    return sum(individual),
  
def main():
    # creator.FitnessMax クラスと、creator.Individual クラスを作成
    creator.create("FitnessMax", base.Fitness, weights=(1.0,))
    creator.create("Individual", list, fitness=creator.FitnessMax)

    toolbox = base.Toolbox()

    # 個体とその集団を作るための関数を作成
    toolbox.register("attr_bool", random.randint, 0, 1)
    toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 100)
    toolbox.register("population", tools.initRepeat, list, toolbox.individual)

    # 遺伝的アルゴリズムにおける評価、交叉、突然変異、選択をおこなう関数を作成
    toolbox.register("evaluate", eval_one_max)
    toolbox.register("mate", tools.cxTwoPoint)
    toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
    toolbox.register("select", tools.selTournament, tournsize=3)

    # 適応度の計算
    pop = toolbox.population(n=300)
    for ind, fit in zip(pop, map(toolbox.evaluate, pop)):
        ind.fitness.values = fit

    fits = [ind.fitness.values[0] for ind in pop]

    # 交叉する確率と突然変異する確率
    CXPB, MUTPB = 0.5, 0.2

    n_epochs = 300
    for epoch in range(n_epochs):
        if not max(fits) < 100:
            break

        if epoch % 10 == 0:
            print(f"--- epoch {epoch} ---")

        # 次世代の個体群を選択後、deep copy
        offsprings = toolbox.select(pop, len(pop))
        offsprings = list(map(toolbox.clone, offsprings))

        # 交叉
        for child1, child2 in zip(offsprings[::2], offsprings[1::2]):
            if random.random() < CXPB:
                toolbox.mate(child1, child2)
                del child1.fitness.values
                del child2.fitness.values

        # 突然変異
        for child in offsprings:
            if random.random() < MUTPB:
                toolbox.mutate(child)
                del child.fitness.values

        invalid_ind = list(filter(lambda ind: not ind.fitness.valid, offsprings))
        # invalid_ind = [offspring for offspring in offsprings if not offspring.fitness.valid]
        fits = map(toolbox.evaluate, invalid_ind)
        for ind, fit in zip(invalid_ind, fits):
            ind.fitness.values = fit

        pop = deepcopy(offsprings)
        fits = [ind.fitness.values[0] for ind in pop]
    
    print(max(fits))

if __name__ == "__main__":
    main()