写一段基于基因估计的python代码

以下是一个简单的基于基因估计的Python代码示例：

import random

# 定义基因序列
GENES = '''abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ 1234567890, .-;:_!"#%&/()=?@${[]}'''

# 定义目标字符串
TARGET = "Hello, World!"

# 定义个体类
class Individual:
    def __init__(self, chromosome):
        self.chromosome = chromosome
        self.fitness = self.cal_fitness()

    def cal_fitness(self):
        fitness = 0
        for i in range(len(self.chromosome)):
            if self.chromosome[i] != TARGET[i]:
                fitness += 1
        return fitness

# 创建种群
def create_population(population_size):
    population = []
    for i in range(population_size):
        chromosome = [random.choice(GENES) for _ in range(len(TARGET))]
        population.append(Individual(chromosome))
    return population

# 选择最适应的个体
def select_parents(population):
    return sorted(population, key=lambda x: x.fitness)[:2]

# 交叉
def crossover(parents):
    # 随机选择交叉点
    crossover_point = random.randint(0, len(TARGET) - 1)
    # 生成子代
    child_chromosome = parents[0].chromosome[:crossover_point] + parents[1].chromosome[crossover_point:]
    return Individual(child_chromosome)

# 变异
def mutate(child):
    # 随机选择变异点
    mutation_point = random.randint(0, len(TARGET) - 1)
    # 生成新的基因
    child_chromosome = list(child.chromosome)
    child_chromosome[mutation_point] = random.choice(GENES)
    return Individual(child_chromosome)

# 运行算法
def genetic_algorithm(population_size):
    population = create_population(population_size)
    generation = 1

    while True:
        print("Generation:", generation)

        # 选择父代
        parent1, parent2 = select_parents(population)
        print("Parent 1:", "".join(parent1.chromosome))
        print("Parent 2:", "".join(parent2.chromosome))

        # 交叉
        child = crossover([parent1, parent2])
        print("Child:", "".join(child.chromosome))

        # 变异
        if random.random() < 0.1:
            child = mutate(child)
            print("Mutated Child:", "".join(child.chromosome))

        # 计算适应度
        child.fitness = child.cal_fitness()
        print("Fitness:", child.fitness)

        # 替换最差的个体
        population.remove(max(population, key=lambda x: x.fitness))
        population.append(child)

        # 如果找到了目标字符串，退出
        if child.fitness == 0:
            break

        generation += 1

    print("Solution found in generation", generation)
    print("".join(child.chromosome))

if __name__ == '__main__':
    genetic_algorithm(100)

这个代码演示了一个简单的基因估计算法，用于寻找一个字符串的近似匹配。算法首先创建一个随机的种群，然后选择最适应的个体进行交叉和变异，以生成新的个体。重复这个过程，直到找到了目标字符串为止。