给我写出遗传算法的计算卸载代码代码

这里简单给出一个遗传算法的计算卸载代码的 Python 代码示例，仅供参考：

import random

# 目标函数：计算卸载后的性能提升
def fitness(solution):
    # TODO: 根据卸载方案计算性能提升
    return performance_increase

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

# 选择操作：轮盘赌选择
def selection(population, fitness_values):
    population_size = len(population)
    fitness_sum = sum(fitness_values)
    selected_population = []
    for i in range(population_size):
        pick = random.uniform(0, fitness_sum)
        current = 0
        for j in range(population_size):
            current += fitness_values[j]
            if current > pick:
                selected_population.append(population[j])
                break
    return selected_population

# 交叉操作：单点交叉
def crossover(parent1, parent2):
    gene_length = len(parent1)
    crossover_point = random.randint(1, gene_length - 1)
    child1 = parent1[:crossover_point] + parent2[crossover_point:]
    child2 = parent2[:crossover_point] + parent1[crossover_point:]
    return child1, child2

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

# 遗传算法求解卸载代码问题
def genetic_algorithm(pop_size, gene_length, max_iteration, mutation_rate):
    # 初始化种群
    population = init_population(pop_size, gene_length)
    # 迭代寻找最优解
    for i in range(max_iteration):
        # 计算适应度
        fitness_values = [fitness(solution) for solution in population]
        # 选择操作
        selected_population = selection(population, fitness_values)
        # 交叉操作
        offspring_population = []
        for j in range(pop_size // 2):
            parent1 = random.choice(selected_population)
            parent2 = random.choice(selected_population)
            child1, child2 = crossover(parent1, parent2)
            offspring_population.extend([child1, child2])
        # 变异操作
        mutated_population = [mutation(solution, mutation_rate) for solution in offspring_population]
        # 更新种群
        population = mutated_population
    # 返回最优解
    best_solution = max(population, key=lambda x: fitness(x))
    return best_solution

需要根据具体情况进行修改和调整，如更改目标函数、调整参数等。