遗传算法Python代码

以下是一个简单的遗传算法Python代码示例：

import random

# 适应度函数
def fitness(individual):
    # 计算个体的适应度值
    return sum(individual)

# 生成初始种群
def generate_population(population_size, individual_size):
    population = []
    for i in range(population_size):
        individual = [random.randint(0, 1) for _ in range(individual_size)]
        population.append(individual)
    return population

# 选择操作
def selection(population):
    # 选择两个个体
    return random.sample(population, 2)

# 交叉操作
def crossover(individual1, individual2):
    # 随机选择交叉点
    crossover_point = random.randint(1, len(individual1) - 1)
    # 交叉
    new_individual1 = individual1[:crossover_point] + individual2[crossover_point:]
    new_individual2 = individual2[:crossover_point] + individual1[crossover_point:]
    return new_individual1, new_individual2

# 变异操作
def mutation(individual, mutation_rate):
    for i in range(len(individual)):
        if random.random() < mutation_rate:
            individual[i] = 1 - individual[i]
    return individual

# 遗传算法主函数
def genetic_algorithm(population_size, individual_size, mutation_rate, generations):
    # 生成初始种群
    population = generate_population(population_size, individual_size)
    for generation in range(generations):
        # 计算种群中每个个体的适应度值
        fitnesses = [fitness(individual) for individual in population]
        # 选择两个个体
        individual1, individual2 = selection(population)
        # 交叉
        new_individual1, new_individual2 = crossover(individual1, individual2)
        # 变异
        new_individual1 = mutation(new_individual1, mutation_rate)
        new_individual2 = mutation(new_individual2, mutation_rate)
        # 计算新个体的适应度值
        new_fitness1 = fitness(new_individual1)
        new_fitness2 = fitness(new_individual2)
        # 替换种群中适应度值最低的两个个体
        min_fitness_index = fitnesses.index(min(fitnesses))
        if new_fitness1 > new_fitness2:
            population[min_fitness_index] = new_individual1
        else:
            population[min_fitness_index] = new_individual2
    # 返回适应度值最高的个体
    return max(population, key=fitness)

# 示例
individual_size = 10
population_size = 100
mutation_rate = 0.01
generations = 100
result = genetic_algorithm(population_size, individual_size, mutation_rate, generations)
print(result)