如何在Python中使用遗传算法进行查找

import random

# 目标字符串
target = "pidancode.com"

# 个体数量
pop_size = 100

# 交叉概率
crossover_prob = 0.8

# 变异概率
mutation_prob = 0.2

# 最大代数
max_gen = 200

# 随机生成一个个体
def generate_individual():
    individual = ""
    for i in range(len(target)):
        individual += chr(random.randint(32, 126))
    return individual

# 初始化种群
def init_population():
    population = []
    for i in range(pop_size):
        population.append(generate_individual())
    return population

# 计算适应度
def fitness(individual):
    score = 0
    for i in range(len(target)):
        if individual[i] == target[i]:
            score += 1
    return score / len(target)

# 选择操作
def selection(population):
    p = []
    for i in range(len(population)):
        p.append(fitness(population[i]))
    p_sum = sum(p)
    for i in range(len(p)):
        p[i] /= p_sum
    selected = []
    for i in range(len(population)):
        r = random.random()
        for j in range(len(population)):
            r -= p[j]
            if r < 0:
                selected.append(population[j])
                break
    return selected

# 交叉操作
def crossover(parent1, parent2):
    if random.random() < crossover_prob:
        point = random.randint(0, len(parent1) - 1)
        child1 = parent1[:point] + parent2[point:]
        child2 = parent2[:point] + parent1[point:]
        return child1, child2
    else:
        return parent1, parent2

# 变异操作
def mutation(individual):
    if random.random() < mutation_prob:
        point = random.randint(0, len(individual) - 1)
        individual = individual[:point] + chr(random.randint(32, 126)) + individual[point + 1:]
    return individual

# 进化操作
def evolution(population):
    next_population = []
    selected = selection(population)
    for i in range(0, len(selected), 2):
        parent1, parent2 = selected[i], selected[i + 1]
        child1, child2 = crossover(parent1, parent2)
        child1 = mutation(child1)
        child2 = mutation(child2)
        next_population.append(child1)
        next_population.append(child2)
    return next_population

# 主函数
def main():
    population = init_population()
    for i in range(max_gen):
        best_individual = max(population, key=fitness)
        print("Generation {}: {}".format(i, best_individual))
        if best_individual == target:
            print("Found!")
            break
        population = evolution(population)

if __name__ == "__main__":
    main()