Python 当解决方案是位数组时，如何进行Mutations ，即解决方案之一的S基因的倒置

反转Mutations 颠倒了基因子集的顺序.它不会将基因的值从0反转到1.也就是说，如果你有一个类似abcd的染色体，那么反转Mutations 会将基因反转为dcba.

要应用将基因从0翻转为1和从1翻转为0的变异运算符，请使用以下代码.它创建新函数flip_mutation()来翻转比特.

import pygad
import numpy as np


def divider(ga_instance):
    return np.max(np.sum(ga_instance.population, axis=1))


def on_start(ga_instance):
    print("on_start()")
    print(f'Начальная популяция:\n {ga_instance.population}')


def fitness_function(ga_instance, solution, _):
    return np.sum(solution) / divider(ga_instance)


def on_fitness(ga_instance, population_fitness):
    print(f'\non_fitness()')
    print(f'Делитель: {divider(ga_instance)}')
    for idx, (instance, fitness) in enumerate(zip(ga_instance.population, ga_instance.last_generation_fitness)):
        print(f'{idx}. {instance}: {fitness}')


def on_parents(ga_instance, selected_parents):
    print("\non_parents()")
    print(f'Выбранные индексы родителей: {ga_instance.last_generation_parents_indices}')
    print(f'Выбранные родители:\n {ga_instance.last_generation_parents}')


def on_crossover(ga_instance, offspring_crossover):
    print("\non_crossover()")
    print(f'Результат кроссинговера:\n {ga_instance.last_generation_offspring_crossover}')


def on_mutation(ga_instance, offspring_mutation):
    print("\non_mutation()")
    print(f'Результат мутации:\n {ga_instance.last_generation_offspring_mutation}')


def on_generation(ga_instance):
    print(f"\non_generation()")
    print("Выведенное поколение:\n ", ga_instance.population)
    sol = ga_instance.best_solution()
    print(f"Лучшее решение: {sol[0]} : {sol[1]}")


def flip_mutation(offspring, ga_instance):
    for idx in range(offspring.shape[0]):
        mutation_gene1 = np.random.randint(low=0, high=np.ceil(offspring.shape[1]/2 + 1), size=1)[0]
        if offspring[idx, mutation_gene1] == 1:
            offspring[idx, mutation_gene1] = 0
        else:
            offspring[idx, mutation_gene1] = 1

    return offspring

ga_instance = pygad.GA(
    num_generations=1,
    num_parents_mating=2,
    fitness_func=fitness_function,
    gene_type=int,
    init_range_low=0,
    init_range_high=2,
    sol_per_pop=10,
    num_genes=8,
    crossover_type='single_point',
    parent_selection_type="rws",
    mutation_type=flip_mutation,
    mutation_num_genes=1,
    on_start=on_start,
    on_fitness=on_fitness,
    on_parents=on_parents,
    on_crossover=on_crossover,
    on_mutation=on_mutation,
    on_generation=on_generation,
)

ga_instance.run()