请用python写一个用遗传算法解决TSP问题的代码

遗传算法解决TSP问题的Python代码示例通常包含以下几个部分：编码、初始化、适应度函数、选择、交叉和变异等。由于篇幅有限，这里提供一个简化版的框架代码：

import random
import numpy as np

# 基本假设：有n个城市，每个城市有坐标(x, y)
def tsp_tour(cities):
    # 这里仅用随机排列作为基础解
    def generate_random_tour():
        return list(range(len(cities)))  # 创建一个随机游览序列

    def tour_distance(tour):
        distance = 0
        for i in range(len(tour)):
            distance += calculate_distance(cities[tour[i]], cities[tour[(i + 1) % len(tour)]])
        return distance

    def calculate_distance(city1, city2):
        x1, y1 = cities[city1]
        x2, y2 = cities[city2]
        return np.sqrt((x2 - x1)**2 + (y2 - y1)**2)

    def selection(population, fitness):
        return random.choices(population, weights=fitness / sum(fitness), k=len(population))

    def crossover(parents):
        child1, child2 = [], []
        point = random.randint(0, len(parents[0]) - 1)
        child1.extend(parents[0][:point] + parents[1][point:])
        child2.extend(parents[1][:point] + parents[0][point:])
        return child1, child2

    def mutate(child):
        if random.random() < mutation_rate:
            swap_index = random.randint(0, len(child) - 1)
            child[swap_index], child[(swap_index + 1) % len(child)] = child[(swap_index + 1) % len(child)], child[swap_index]
        return child

    # 初始化一个固定大小的种群
    population_size = 100
    population = [generate_random_tour() for _ in range(population_size)]
    
    # 假设一个简单的适应度函数是路径长度
    fitness = [tour_distance(p) for p in population]

    for generation in range(max_generations):
        new_population = []

        while len(new_population) < population_size:
            parents = selection(population, fitness)
            offspring = crossover(parents)
            offspring[0] = mutate(offspring[0])
            offspring[1] = mutate(offspring[1])

            new_population.append(min(offspring, key=tour_distance))
        
        # 更新种群
        population = new_population
        best_tour = min(population, key=tour_distance)
        print(f"Generation {generation+1}: Best Tour Length = {tour_distance(best_tour)}")

    return best_tour

# 假设cities是一个包含城市坐标列表
cities = ...  # 根据实际需求填充城市坐标数据
mutation_rate = 0.01
max_generations = 1000

best_tour = tsp_tour(cities)

注意这只是一个简化的示例，实际应用中可能需要更复杂的编码、选择、交叉和变异策略，以及自适应终止条件等。此外，这个代码也不能保证找到最优解，因为遗传算法往往只能找到近似最优解。