python遗传算法代码
    以下是一个简单的遗传算法的Python代码：
    ```python
    import random
    # 初始化种
    def init_population(population_size, chromosome_length):
    population = []
    for i in range(population_size):
    chromosome = [random.randint(0, 1) for _ in range(chromosome_length)]
    population.append(chromosome)
    return population
    # 计算适应度值
    def calculate_fitness(chromosome):
random python
    fitness = sum(chromosome)
    return fitness
    # 选择
    def selection(population, fitness):
    max_fitness = max(fitness)
    max_index = fitness.index(max_fitness)
    return population[max_index]
    # 交叉
    def crossover(parent1, parent2):
    crossover_point = random.randint(1, len(parent1) - 1)
    child1 = parent1[:crossover_point] + parent2[crossover_point:]
    child2 = parent2[:crossover_point] + parent1[crossover_point:]
    return child1, child2
    # 变异
    def mutation(chromosome, mutation_rate):
    for i in range(len(chromosome)):
    if random.random() < mutation_rate:
    chromosome[i] = 1 - chromosome[i]
    return chromosome
    # 遗传算法主函数
    def genetic_algorithm(population_size, chromosome_length, mutation_rate, generations):
    population = init_population(population_size, chromosome_length)
    for _ in range(generations):
    fitness = [calculate_fitness(chromosome) for chromosome in population]
    new_population = []
    for _ in range(population_size // 2):
    parent1 = selection(population, fitness)
    parent2 = selection(population, fitness)
    child1, child2 = crossover(parent1, parent2)
    child1 = mutation(child1, mutation_rate)
    child2 = mutation(child2, mutation_rate)
    new_population.append(child1)
    new_population.append(child2)
    population = new_population
    best_chromosome = selection(population, fitness)
    best_fitness = calculate_fitness(best_chromosome)
    return best_chromosome, best_fitness
    # 测试
    population_size = 50
    chromosome_length = 10
    mutation_rate = 0.01
    generations = 100
    best_chromosome, best_fitness = genetic_algorithm(population_size, chromosome_length, mutation_rate, generations)
    print("Best Chromoso" best_chromosome)
    print("Best Fitness:" best_fitness)
    ```
    这段代码实现了一个简单的遗传算法，通过随机生成初始种，利用适应度函数计算每个染体的适应度值，然后进行选择、交叉和变异操作，最终得到最优解。

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