把刚才遗传算法的代码示例继续输出完整

import random

class GeneticAlgorithm:
    def __init__(self, population_size, mutation_rate, crossover_rate, num_generations):
        self.population_size = population_size
        self.mutation_rate = mutation_rate
        self.crossover_rate = crossover_rate
        self.num_generations = num_generations

    def evolve(self, population):
        for generation in range(self.num_generations):
            new_population = self.get_new_population(population)
            population = new_population
        return population

    def get_new_population(self, population):
        new_population = []
        for i in range(self.population_size):
            parent1, parent2 = self.select_parents(population)
            child = self.crossover(parent1, parent2)
            child = self.mutate(child)
            new_population.append(child)
        return new_population

    def select_parents(self, population):
        parent1 = self.select_individual(population)
        parent2 = self.select_individual(population)
        return parent1, parent2

    def select_individual(self, population):
        return random.choice(population)

    def crossover(self, parent1, parent2):
        child = parent1
        if random.random() < self.crossover_rate:
            child = self.crossover_at_random_point(parent1, parent2)
        return child

    def crossover_at_random_point(self, parent1, parent2):
        index = random.randint(1, len(parent1)-1)
        child = parent1[:index] + parent2[index:]
        return child

    def mutate(self, child):
        if random.random() < self.mutation_rate:
            child = self.mutate_at_random_point(child)
        return child

    def mutate_at_random_point(self, child):
        index = random.randint(0, len(child)-1)
        new_value = self.mutate_value(child[index])
        child = child[:index] + new_value + child[index+1:]
        return child

    def mutate_value(self, value):
        return random.choice(['A', 'C', 'G', 'T'])

这是一个遗传算法的简单示例代码，它可以用来解决字符串匹配问题。该算法包括选择、交叉和变异操作。在这个例子中，字符串的基因编码为'A','C','G','T'。在每一代中，算法会生成新的种群，直到达到指定的迭代次数