帮我举一个遗传算法的例子，并用python生成代码

一个经典的遗传算法示例是TSP（旅行商问题）。TSP是一个NP难题，因此使用遗传算法来解决它是非常有效的。

以下是一个使用Python实现TSP遗传算法的示例代码：

import random
import numpy as np

# 遗传算法类
class GeneticAlgorithm:
    def __init__(self, population_size, mutation_rate, elitism):
        self.population_size = population_size
        self.mutation_rate = mutation_rate
        self.elitism = elitism

    # 初始化种群
    def init_population(self, chromosome_length):
        population = []
        for i in range(self.population_size):
            chromosome = [i for i in range(chromosome_length)]
            random.shuffle(chromosome)
            population.append(chromosome)
        return population

    # 计算适应度
    def calculate_fitness(self, chromosome, distance_matrix):
        fitness = 0
        for i in range(len(chromosome) - 1):
            fitness += distance_matrix[chromosome[i]][chromosome[i+1]]
        fitness += distance_matrix[chromosome[-1]][chromosome[0]]
        return fitness

    # 选择
    def selection(self, population, distance_matrix):
        fitnesses = [self.calculate_fitness(chromosome, distance_matrix) for chromosome in population]
        sorted_indexes = np.argsort(fitnesses)
        sorted_population = [population[i] for i in sorted_indexes]
        return sorted_population[:self.elitism] + random.choices(sorted_population[self.elitism:], k=self.population_size-self.elitism)

    # 交叉
    def crossover(self, parent1, parent2):
        child = [-1] * len(parent1)
        start = random.randint(0, len(parent1) - 1)
        end = random.randint(0, len(parent1) - 1)
        if start > end:
            start, end = end, start
        for i in range(start, end+1):
            child[i] = parent1[i]
        j = 0
        for i in range(len(parent2)):
            if child[j] == -1:
                if parent2[i] not in child:
                    child[j] = parent2[i]
                    j += 1
            else:
                j += 1
        return child

    # 变异
    def mutate(self, chromosome):
        if random.random() < self.mutation_rate:
            index1 = random.randint(0, len(chromosome) - 1)
            index2 = random.randint(0, len(chromosome) - 1)
            chromosome[index1], chromosome[index2] = chromosome[index2], chromosome[index1]

    # 运行遗传算法
    def run(self, distance_matrix, num_generations):
        population = self.init_population(len(distance_matrix))
        for i in range(num_generations):
            population = self.selection(population, distance_matrix)
            new_population = []
            while len(new_population) < self.population_size:
                parent1, parent2 = random.sample(population, 2)
                child = self.crossover(parent1, parent2)
                self.mutate(child)
                new_population.append(child)
            population = new_population
        best_individual = min(population, key=lambda chromosome: self.calculate_fitness(chromosome, distance_matrix))
        return best_individual, self.calculate_fitness(best_individual, distance_matrix)

# 测试
if __name__ == '__main__':
    distance_matrix = [
        [0, 2, 9, 10],
        [1, 0, 6, 4],
        [15, 7, 0, 8],
        [6, 3, 12, 0]
    ]
    ga = GeneticAlgorithm(population_size=100, mutation_rate=0.01, elitism=2)
    best_individual, best_fitness = ga.run(distance_matrix, num_generations=1000)
    print('Best individual:', best_individual)
    print('Best fitness:', best_fitness)

这个代码实现了一个遗传算法来解决TSP问题。它首先初始化一个种群，然后在每一代中进行选择、交叉和变异操作。最后，它返回最佳个体和适应度。