python实现遗传算法代码

遗传算法是一种优化算法，可以用来求解函数最大值或最小值问题。Python是一种非常适合编写遗传算法的编程语言。以下是一个简单的Python实现遗传算法的例子：

1. 初始化种群：生成一组随机的个体作为初始种群，每个个体都是由一组基因组成的。

2. 评估适应度：对每个个体计算其适应度，适应度越高表示该个体越优秀。

3. 选择：从当前种群中选择一部分个体作为下一代的父代，通常采用轮盘赌选择或者锦标赛选择方法。

4. 交叉：对父代个体进行交叉操作，产生新的后代个体。

5. 变异：对后代个体进行变异操作，引入一些随机因素，增加种群多样性。

6. 更新种群：用新的后代个体替换掉原来的父代个体，形成新的种群。

7. 重复上述过程，直到达到停止条件。

具体的Python代码如下：

import random

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

# 计算适应度
def fitness(chromosome):
    x = int(''.join([str(i) for i in chromosome]), 2)
    return x * x

# 选择
def selection(population, fitness):
    fitness_sum = sum(fitness)
    probability = [f / fitness_sum for f in fitness]
    probability_sum = [sum(probability[:i+1]) for i in range(len(probability))]
    new_population = []
    for i in range(len(population)):
        r = random.random()
        for j in range(len(probability_sum)):
            if r <= probability_sum[j]:
                new_population.append(population[j])
                break
    return new_population

# 交叉
def crossover(parent1, parent2, pc):
    if random.random() > pc:
        return parent1, parent2
    pos = random.randint(0, len(parent1)-1)
    child1 = parent1[:pos] + parent2[pos:]
    child2 = parent2[:pos] + parent1[pos:]
    return child1, child2

# 变异
def mutation(child, pm):
    for i in range(len(child)):
        if random.random() < pm:
            child[i] = 1 - child[i]
    return child

# 遗传算法主程序
def genetic_algorithm(pop_size, chrom_size, pc, pm, max_iter):
    population = init_population(pop_size, chrom_size)
    best_fitness = -float('inf')
    best_chromosome = None
    for i in range(max_iter):
        fitness_list = [fitness(chromosome) for chromosome in population]
        if max(fitness_list) > best_fitness:
            best_fitness = max(fitness_list)
            index = fitness_list.index(best_fitness)
            best_chromosome = population[index]
        population = selection(population, fitness_list)
        new_population = []
        for j in range(0, len(population), 2):
            if j+1 < len(population):
                child1, child2 = crossover(population[j], population[j+1], pc)
                child1 = mutation(child1, pm)
                child2 = mutation(child2, pm)
                new_population.append(child1)
                new_population.append(child2)
        population = new_population
    return best_chromosome, best_fitness

# 测试代码
if __name__ == '__main__':
    pop_size = 100
    chrom_size = 8
    pc = 0.8
    pm = 0.01
    max_iter = 100
    best_chromosome, best_fitness = genetic_algorithm(pop_size, chrom_size, pc, pm, max_iter)
    print('Best Chromosome:', best_chromosome)
    print('Best Fitness:', best_fitness)