给出实现上面问题的python代码

为了实现上述遗传算法路径规划的问题，我们可以采用Python编写一个简单版本。首先，需要导入必要的库并定义基本的类和函数。这里是一个简化的例子，假设我们使用`numpy`处理矩阵，以及`random`模块生成随机路径和进行选择、交叉、变异等操作：

import numpy as np
import random

# 定义网格地图类
class GridMap:
    def __init__(self, width, height, obstacles):
        self.map = np.zeros((width, height), dtype=int)
        for x, y in obstacles:
            self.map[x][y] = -1  # 障碍位置设为负数

# 遗传算法核心类
class GeneticAlgorithm:
    def __init__(self, map_data, start, end, pop_size=100, mutation_rate=0.01):
        self.grid_map = GridMap(*map_data)
        self.start, self.end = start, end
        self.pop_size, self.mutation_rate = pop_size, mutation_rate
        self.population = self.create_population()

    def create_population(self):
        paths = []
        for _ in range(self.pop_size):
            path = [self.start]
            current = self.start
            while not self.is_goal(current):
                possible_neighbors = self.get_neighbors(current)
                if possible_neighbors:
                    next_step = random.choice(possible_neighbors)
                else:
                    break
                path.append(next_step)
                current = next_step
            paths.append(path)
        return paths

    def fitness_function(self, path):
        return sum([self.grid_map.map[path[i]][path[i+1]] for i in range(len(path)-1)])

    # ...继续添加选择、交叉和变异的方法...

    # 辅助方法
    def is_goal(self, position):
        return position == self.end and all(self.grid_map.map[position])

    def get_neighbors(self, position):
        valid_neighbors = [(position[0]+dx, position[1]+dy) for dx, dy in [(-1, 0), (1, 0), (0, -1), (0, 1)] if self.grid_map.map[position[0]+dx][position[1]+dy] >= 0]
        return valid_neighbors

# 示例使用
ga = GeneticAlgorithm((10, 10), (0, 0), (9, 9))  # 假设10x10的地图，起点(0,0)，终点(9,9)
best_path = ga.run()  # 调用算法找出最佳路径

这只是一个基本框架，实际应用中可能还需要完善循环终止条件、优化算法细节等。