Pythonを使用して遺伝的アルゴリズムを実装するにはどうすればよいですか?

import random def generate_individual(length): return [random.randint(0, 1) for _ in range(length)] def evaluate_fitness(individual): return sum(individual) def selection(population, num_parents): population.sort(key=lambda x: evaluate_fitness(x), reverse=True) return population[:num_parents] def crossover(parents, num_offsprings): offsprings = [] for _ in range(num_offsprings): parent1, parent2 = random.sample(parents, 2) cut_point = random.randint(1, len(parent1) - 1) offspring = parent1[:cut_point] + parent2[cut_point:] offsprings.append(offspring) return offsprings def mutation(offsprings, mutation_rate): for i in range(len(offsprings)): if random.random() < mutation_rate: index = random.randint(0, len(offsprings[i]) - 1) offsprings[i][index] = 1 - offsprings[i][index] return offsprings def genetic_algorithm(length, population_size, num_parents, num_offsprings, mutation_rate, num_generations): population = [generate_individual(length) for _ in range(population_size)] for _ in range(num_generations): parents = selection(population, num_parents) offsprings = crossover(parents, num_offsprings) offsprings = mutation(offsprings, mutation_rate) population = parents + offsprings best_individual = max(population, key=lambda x: evaluate_fitness(x)) return best_individual # 示例运行 length = 10 population_size = 50 num_parents = 20 num_offsprings = 20 mutation_rate = 0.1 num_generations = 100 best_individual = genetic_algorithm(length, population_size, num_parents, num_offsprings, mutation_rate, num_generations) print(f"最优解为：{best_individual}")