免疫克隆选择算法python代码

以下是一个简单的免疫克隆选择算法的 Python 代码示例：

import random

# 定义适应度函数
def fitness(individual):
    # TODO: 根据个体的表现计算适应度
    return 0

# 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 j in range(gene_size)]
        population.append(individual)
    return population

# 克隆操作
def clone(population, clone_size):
    clones = []
    for i in range(clone_size):
        # 选择适应度最高的个体进行克隆
        best_individual = max(population, key=fitness)
        clone = best_individual.copy()
        # 对克隆进行变异操作
        for j in range(len(clone)):
            if random.random() < 0.1:
                clone[j] = 1 - clone[j]
        clones.append(clone)
    return clones

# 选择操作
def select(population, select_size):
    # 根据适应度排序
    sorted_population = sorted(population, key=fitness, reverse=True)
    # 选择适应度最高的个体
    selected = sorted_population[:select_size]
    return selected

# 免疫克隆选择算法
def ics_algorithm(pop_size, gene_size, clone_size, select_size, max_iter):
    # 初始化种群
    population = init_population(pop_size, gene_size)
    for i in range(max_iter):
        # 克隆操作
        clones = clone(population, clone_size)
        # 合并种群和克隆
        population.extend(clones)
        # 选择操作
        population = select(population, pop_size)
    # 返回适应度最高的个体
    best_individual = max(population, key=fitness)
    return best_individual

# 示例运行
best_individual = ics_algorithm(pop_size=100, gene_size=10, clone_size=20, select_size=50, max_iter=100)
print(best_individual)

请注意，这只是一个简单的示例代码，实际应用中需要根据具体问题进行修改和优化。