帮我写一段实现遗传算法的python代码

遗传算法是一种模拟自然选择过程的优化搜索算法，它通过复制、变异和交叉操作来寻找最优解。下面是一个简单的Python实现遗传算法的框架示例：

import random

# 定义问题域的个体表示（假设我们解决的是0-9间的整数加法）
class Individual:
    def __init__(self):
        self.genes = [random.randint(0, 9) for _ in range(10)]  # 初始基因由随机数构成

    def fitness(self):  # 健康度函数，这里简单地设置为基因之和
        return sum(self.genes)

# 初始化种群
def create_population(size):
    return [Individual() for _ in range(size)]

# 配对选择
def selection(population):
    fit_sum = sum(indiv.fitness() for indiv in population)
    probabilities = [indiv.fitness() / fit_sum for indiv in population]
    return random.choices(population, probabilities, k=len(population))

# 交叉操作
def crossover(parent1, parent2):
    offspring = parent1.genes.copy()
    crossover_point = random.randint(1, len(offspring)-1)
    offspring[crossover_point:] = parent2.genes[crossover_point:]
    return Individual(offspring)

# 变异操作
def mutation(individual, mutation_rate=0.1):
    for i in range(len(individual.genes)):
        if random.random() < mutation_rate:
            individual.genes[i] = random.randint(0, 9)
    return individual

# 主遗传循环
def genetic_algorithm(population_size, generations, crossover_rate, mutation_rate):
    population = create_population(population_size)
    
    for _ in range(generations):
        parents = selection(population)
        offspring = [crossover(parents[i], parents[(i+1)%population_size]) for i in range(0, len(parents), 2)]
        offspring += [mutation(indiv) for indiv in offspring]
        
        # 更新种群
        population = [max(population, key=Individual.fitness) if random.random() > 0.5 else indiv for indiv in offspring]

    best_individual = max(population, key=Individual.fitness)
    return best_individual, best_individual.fitness()

# 调用遗传算法
best_solution, best_fitness = genetic_algorithm(100, 100, 0.8, 0.1)
print(f"最优解：{best_solution.genes}, 最优值：{best_fitness}")