遗传算法python代码

这里是一个简单的遗传算法 Python 代码示例：

import random

def fitness(candidate):
    # 返回候选解的适应度值
    # 这里只是一个简单的示例
    return sum(candidate)

def generate_candidate(length):
    # 生成一个随机的候选解
    return [random.randint(0, 1) for _ in range(length)]

def mutate(candidate, mutation_rate):
    # 对候选解进行突变
    for i in range(len(candidate)):
        if random.random() < mutation_rate:
            candidate[i] = 1 - candidate[i]
    return candidate

def crossover(parent1, parent2):
    # 对两个父代候选解进行交叉
    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

def genetic_algorithm(population_size, mutation_rate, generations, selection_size):
    # 生成初始种群
    population = [generate_candidate(10) for _ in range(population_size)]

    for i in range(generations):
        # 计算每个候选解的适应度值
        fitness_scores = [fitness(candidate) for candidate in population]

        # 选择最适应的候选解
        selected_candidates = [population[i] for i in sorted(range(len(fitness_scores)), key=lambda x: fitness_scores[x], reverse=True)[:selection_size]]

        # 生成下一代候选解
        children = []
        while len(children) < population_size - selection_size:
            # 随机选择两个父代候选解
            parent1 = random.choice(selected_candidates)
            parent2 = random.choice(selected_candidates)

            # 交叉生成两个子代候选解
            child1, child2 = crossover(parent1, parent2)

            # 对子代候选解进行突变
            child1 = mutate(child1, mutation_rate)
            child2 = mutate(child2, mutation_rate)

            # 将子代候选解加入下一代种群
            children.append(child1)
            if len(children) < population_size - selection_size:
                children.append(child2)

        # 将最适应的候选解加入下一代种群
        population = selected_candidates + children

    # 返回最终最适应的候选解
    return max(population, key=fitness)

这是一个基本的遗传算法实现，可以根据需要进行修改和扩展。