How do I implement genetic algorithm on grid board to find optimal path

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Python

algorithm

genetic

Hello
I am preparing algorithms for optimal path finding in terrain with obstacles. Till now i implemented Dijsktra and A* algorithms. Now i have to implement genetic algorithm and I have problem. Firstly I will show you how my map representation looks. There are 7 different kinds of terrain (0- start, 7- end, 1-4 normal which can be passed, 5-6 cannot pass). Here is code for that in Python (the most important part of code, in my opinion, to understand problem is function neighbors:

class Graph():
    def __init__(self, x=10, y=10):
        self.width = x
        self.height = y
        self.board = ((1, 1, 1, 5, 1, 1, 1, 1, 1, 7),
                      (1, 1, 1, 5, 1, 1, 1, 1, 1, 1),
                      (1, 1, 1, 5, 1, 5, 1, 1, 1, 1),
                      (0, 1, 1, 1, 1, 5, 1, 1, 1, 1),
                      (1, 1, 1, 1, 1, 5, 1, 1, 1, 1),
                      (1, 1, 1, 1, 1, 1, 1, 1, 1, 1),
                      (1, 1, 1, 1, 1, 1, 1, 1, 1, 1),
                      (1, 1, 1, 1, 1, 1, 1, 1, 1, 1),
                      (1, 1, 1, 1, 1, 1, 1, 1, 1, 1),
                      (1, 1, 1, 1, 1, 1, 1, 1, 1, 1))
        self.time = {0: None,
                     1: 1,
                     2: 4,
                     3: 7,
                     4: 4,
                     7: 1}
    def cost(self, id):
        (x, y)= id
        return self.time.get(self.board[y][x])

    def canPass(self, id):
        (x, y) = id
        return self.board[y][x] != 5 and self.board[y][x] != 6 and self.board[y][x] != 0

    def inBounds(self, id):
        (x, y) = id
        return 0 <= x < self.width and 0 <= y < self.height

    def neighbors(self, id):
        (x, y) = id
        nodes = [(x-1, y), (x, y-1), (x+1, y), (x, y+1)]
        nodes = filter(self.inBounds, nodes)
        nodes = filter(self.canPass, nodes)
        return nodes

I have no idea how to implement genetic algorithm from theoritical point because of my map and neighbor representation and i cannot change them.

What I have tried:

I prepared starting population using modification of my A* which find nearly the easiest connection from start to end without checking cost of that. Here's code

def heuristic(a, b):
    (x1, y1) = a
    (x2, y2) = b
    return abs(x1 - x2) + abs(y1 - y2)

def StartingPopulation(graph, start, goal):
    (x, y) = start
    frontier = PriorityQueue()
    frontier.put(start, 0)
    came_from = {}
    cost_so_far = [[0 for i in xrange(10)] for j in xrange(10)]
    came_from[start] = None
    cost_so_far[y][x] = 0
    while not frontier.empty():
        current = frontier.get()
        (y1, x1) = current
        if (y1, x1) == goal:
            break
        for next in graph.neighbors(current):
            new_cost = cost_so_far[x1][y1] + graph.cost(next)
            (y2, x2) = next
            if cost_so_far[x2][y2] == 0 or new_cost < cost_so_far[x2][y2]:
                cost_so_far[x2][y2] = new_cost
                priority = new_cost + heuristic(goal, next)
                frontier.put(next, priority)
                came_from[next] = current
    return came_from, cost_so_far

That's all I invent. I have no idea how to do other steps for genetic algorithm like selection, crossover and mutation on data i have. I hope you can guide me and give some hints (if there is full code for what I need it would be also good to check and learn from it)