帮我写一个遗传代码

以下是一个简单的遗传算法代码：

import random

# 遗传算法参数
POPULATION_SIZE = 100
MUTATION_RATE = 0.1
CROSSOVER_RATE = 0.8
GENERATIONS = 50

# 目标函数
def fitness(chromosome):
    # 计算染色体的适应度，返回一个浮点数
    pass

# 初始化种群
def initialize_population():
    # 随机生成初始种群，返回一个二维列表
    population = []
    for i in range(POPULATION_SIZE):
        chromosome = []
        for j in range(chromosome_length):
            gene = random.randint(0, 1)
            chromosome.append(gene)
        population.append(chromosome)
    return population

# 选择
def selection(population):
    # 选择一部分染色体，返回一个列表
    pass

# 交叉
def crossover(parent1, parent2):
    # 对两个染色体进行交叉操作，返回两个新的染色体
    pass

# 变异
def mutation(chromosome):
    # 对染色体进行变异操作，返回一个新的染色体
    pass

# 进化
def evolve(population):
    # 进化种群，返回一个新的种群
    pass

# 主函数
def main():
    # 初始化种群
    population = initialize_population()

    # 进化
    for generation in range(GENERATIONS):
        # 计算适应度
        fitness_values = [fitness(chromosome) for chromosome in population]

        # 选择
        selected_population = selection(population)

        # 交叉
        offspring_population = []
        for i in range(0, POPULATION_SIZE, 2):
            if random.random() < CROSSOVER_RATE:
                parent1 = selected_population[i]
                parent2 = selected_population[i+1]
                offspring1, offspring2 = crossover(parent1, parent2)
                offspring_population.append(offspring1)
                offspring_population.append(offspring2)
            else:
                offspring_population.append(selected_population[i])
                offspring_population.append(selected_population[i+1])

        # 变异
        for i in range(len(offspring_population)):
            if random.random() < MUTATION_RATE:
                offspring_population[i] = mutation(offspring_population[i])

        # 用后代替换父代
        population = offspring_population

        # 输出当前种群中适应度最高的染色体
        max_fitness = max(fitness_values)
        best_chromosome = population[fitness_values.index(max_fitness)]
        print("Generation:", generation+1)
        print("Best chromosome:", best_chromosome)
        print("Fitness value:", max_fitness)

if __name__ == "__main__":
    main()

这是一个简单的遗传算法代码，你需要根据具体的问题进行修改和优化。