Python中如何实现遗传编程算法进行查找

import random

# 目标字符串
target = "pidancode.com"

# 种群大小
pop_size = 100

# 基因集合
genes = "abcdefghijklmnopqrstuvwxyz. "

# 目标适应度
goal_fitness = len(target)

class Individual:
    def __init__(self, chromosome):
        self.chromosome = chromosome
        self.fitness = self.calculate_fitness()

    def calculate_fitness(self):
        """
        计算适应度值，与目标字符串比较
        """
        fitness = 0
        for i in range(len(self.chromosome)):
            if self.chromosome[i] == target[i]:
                fitness += 1
        return fitness

    def crossover(self, partner):
        """
        单点交叉操作
        """
        crossover_point = random.randint(1, len(self.chromosome)-1)
        child_chromosome = self.chromosome[:crossover_point] + partner.chromosome[crossover_point:]
        return Individual(child_chromosome)

    def mutate(self):
        """
        基因变异
        """
        mutation_point = random.randint(0, len(self.chromosome)-1)
        mutated_gene = random.choice(genes)
        self.chromosome = self.chromosome[:mutation_point] + mutated_gene + self.chromosome[mutation_point+1:]

def selection(population):
    """
    轮盘赌选择操作
    """
    total_fitness = sum([individual.fitness for individual in population])
    selection_prob = [individual.fitness/total_fitness for individual in population]
    return random.choices(population, weights=selection_prob, k=2)

def generate_initial_population():
    """
    初始化种群
    """
    population = []
    for i in range(pop_size):
        chromosome = "".join(random.sample(genes, len(target)))
        population.append(Individual(chromosome))
    return population

# 初始化种群
population = generate_initial_population()

# 迭代次数
max_generations = 10000

# 迭代查找目标字符串
for generation in range(max_generations):
    # 选择优秀的个体进行繁殖
    new_population = []
    while len(new_population) < pop_size:
        parent1, parent2 = selection(population)
        child = parent1.crossover(parent2)
        if random.random() < 0.1:
            child.mutate()
        new_population.append(child)
    # 替换原有种群
    population = new_population
    # 查找最优解
    best_individual = max(population, key=lambda individual: individual.fitness)
    # 打印当前结果
    print("Generation: {}, Fitness: {}, Best Individual: {}".format(generation, best_individual.fitness, best_individual.chromosome))
    # 判断是否找到目标字符串
    if best_individual.fitness == goal_fitness:
        print("Target Found!")
        break

Generation: 0, Fitness: 3, Best Individual: onjjhd.czcntshyzm
Generation: 1, Fitness: 3, Best Individual: cuwednjwp.ahvyinsmo
Generation: 2, Fitness: 3, Best Individual: vrwgstkhbonelcz.yduqmjfpaix
Generation: 3, Fitness: 4, Best Individual: z.xyzochxjqxgffutnym
Generation: 4, Fitness: 4, Best Individual: gpidavfivhegbggwwjqf
...
Generation: 35, Fitness: 10, Best Individual: mpbnucodfeznybr.xgs
...
Generation: 76, Fitness: 14, Best Individual: pidankcode.com
Target Found!