Python遗传算法的代码

以下是一个简单的Python遗传算法的代码示例：

import random

# 定义目标函数
def fitness(individual):
    return sum(individual)

# 初始化种群
def init_population(pop_size, gene_size):
    population = []
    for i in range(pop_size):
        individual = [random.randint(0, 1) for _ in range(gene_size)]
        population.append(individual)
    return population

# 选择操作
def selection(population):
    fitnesses = [fitness(individual) for individual in population]
    total_fitness = sum(fitnesses)
    probabilities = [fitness / total_fitness for fitness in fitnesses]
    selected = random.choices(population, probabilities, k=2)
    return selected[0], selected[1]

# 交叉操作
def crossover(individual1, individual2):
    crossover_point = random.randint(1, len(individual1) - 1)
    child1 = individual1[:crossover_point] + individual2[crossover_point:]
    child2 = individual2[:crossover_point] + individual1[crossover_point:]
    return child1, child2

# 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(pop_size, gene_size, mutation_rate, generations):
    population = init_population(pop_size, gene_size)
    for i in range(generations):
        parent1, parent2 = selection(population)
        child1, child2 = crossover(parent1, parent2)
        child1 = mutation(child1, mutation_rate)
        child2 = mutation(child2, mutation_rate)
        population.extend([child1, child2])
        population = sorted(population, key=lambda x: fitness(x), reverse=True)
        population = population[:pop_size]
    return population[0]

# 测试
best_individual = genetic_algorithm(pop_size=100, gene_size=10, mutation_rate=0.01, generations=100)
print(best_individual)

该代码实现了一个简单的二进制遗传算法，用于求解一个长度为10的二进制向量中所有元素之和最大的向量。其中，`init_population`函数用于初始化种群，`selection`函数用于选择操作，`crossover`函数用于交叉操作，`mutation`函数用于变异操作，`genetic_algorithm`函数是遗传算法的主函数。