多目标遗传算法 python代码

当然，我可以为你提供一个基本的多目标遗传算法的Python代码示例。以下是一个简单的实现：

import random

# 定义种群大小、染色体长度、变异率和交叉率
pop_size = 50
chrom_length = 10
mutation_rate = 0.01
crossover_rate = 0.6

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

# 计算适应度
def evaluate(chromosome):
    fitness1 = sum(chromosome)
    fitness2 = len(chromosome) - fitness1
    return fitness1, fitness2

# 选择操作
def selection(population):
    fitness_values = [evaluate(chromosome) for chromosome in population]
    total_fitness = sum(sum(fitness) for fitness in fitness_values)
    probabilities = [sum(fitness) / total_fitness for fitness in fitness_values]

    # 轮盘赌选择
    selected_indices = random.choices(range(pop_size), probabilities, k=pop_size)
    selected_population = [population[i] for i in selected_indices]
    return selected_population

# 交叉操作
def crossover(population):
    offspring = []
    for i in range(0, pop_size, 2):
        parent1 = population[i]
        parent2 = population[i+1]
        if random.random() < crossover_rate:
            crossover_point = random.randint(1, chrom_length-1)
            child1 = parent1[:crossover_point] + parent2[crossover_point:]
            child2 = parent2[:crossover_point] + parent1[crossover_point:]
        else:
            child1 = parent1
            child2 = parent2
        offspring.append(child1)
        offspring.append(child2)
    return offspring

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

# 主循环
def genetic_algorithm(pop_size, chrom_length):
    population = init_population(pop_size, chrom_length)
    num_generations = 100

    for _ in range(num_generations):
        selected_population = selection(population)
        offspring = crossover(selected_population)
        mutated_offspring = mutation(offspring)
        population = mutated_offspring

    # 打印最终种群及其适应度
    final_fitness_values = [evaluate(chromosome) for chromosome in population]
    print("Final Population:")
    for chromosome, fitness in zip(population, final_fitness_values):
        print(chromosome, fitness)

# 运行遗传算法
genetic_algorithm(pop_size, chrom_length)

这只是一个简单的多目标遗传算法的实现示例，你可以根据自己的需求进行修改和扩展。请注意，此代码只是基本示例，并没有考虑算法的性能和优化。在实际应用中，你可能需要进行更多的调整和改进。