diff --git a/astar2.py b/astar2.py
index 415e2c5..2bab99d 100644
--- a/astar2.py
+++ b/astar2.py
@@ -263,6 +263,22 @@ class SweeperAgent:
                     a_column = column
                     location = tuple([a_row, a_column])
                     allowed_points.add(location)
+    
+    @staticmethod
+    def set_allowed_for_genetic(allowed_points, map_location):
+        temp_map = [list(item) for item in SweeperAgent.loadMap(map_location)]
+
+        a_row = 0
+        a_column = 0
+
+        for row in range(MAP_SIZE):
+            for column, pos in enumerate(temp_map[row]):
+                if pos == "." or pos == 'x': 
+                    a_row = row
+                    a_column = column
+                    location = tuple([a_row, a_column])
+                    allowed_points.add(location)
+
 
     @staticmethod
     def set_puddles(puddle_points):
@@ -351,6 +367,34 @@ class SweeperAgent:
 
         return orientation
 
+    @staticmethod
+    def run_manual(self,
+        given_orientation,
+        given_goal_orientation,
+        x_my_location,
+        y_my_location,
+        x_goal_location,
+        y_goal_location,
+        map_location
+        ):
+
+        self.orientation = given_orientation
+        goal_orientation = given_goal_orientation
+
+        SweeperAgent.set_allowed(self.set_allowed_for_genetic(map_location))
+
+        x = x_my_location
+        y = y_my_location
+
+        x1 = x_goal_location
+        x2 = y_goal_location
+
+        agent_position = AgentPosition(x, y, self.orientation)
+        goal_position = AgentPosition(x1, x2, goal_orientation)
+
+        return len(self.plan_route(agent_position, goal_position, self.allowed_points, self.puddle_points))
+
+
         
     @staticmethod
     def run(self):
diff --git a/genetic.py b/genetic.py
new file mode 100644
index 0000000..03936db
--- /dev/null
+++ b/genetic.py
@@ -0,0 +1,278 @@
+import random
+
+class Genetic():
+    pass
+
+
+
+
+
+
+    def calc_cost(self, mines_list):
+        self.mined_tile_count = len(mines_list)
+        total_cost = 0
+        i = 0
+        while i < (self.mined_tile_count - 1):
+            total_cost = total_cost + self.manhattan_cost_counter(mines_list, i)
+            i = i + 1
+        
+        return total_cost
+
+    def manhattan_cost_counter(self, mines_list, i):
+
+        
+        x1, y1 = mines_list[i][0], mines_list[i][1]
+        x2, y2 = mines_list[i+1][0], mines_list[i+1][1]
+
+
+        return abs(x2 - x1) + abs(y2 - y1)
+
+    def population_initialization(self, mines_list, i):
+        first_mine = mines_list[0]
+        x = len(mines_list)-1
+        shuffled_coordinates = []
+        while x > 0:
+            shuffled_coordinates.append(mines_list[x])
+            x = x - 1
+        
+        mine_chromosome = []
+        while i > 0:
+            child = random.sample(shuffled_coordinates, len(shuffled_coordinates))
+            child.insert(0, first_mine)
+            #child.insert(x, first_mine)
+            mine_chromosome.append(child)
+            i = i - 1
+        return mine_chromosome
+
+    def fitness_function(self, generations):
+        total_cost = 0
+        chromosome_count = 0
+        chromosome_costs = []
+        y = 0
+        #problem - wyjście poza liste
+        if len(generations) == 0:
+            return
+
+        first_cost = self.calc_cost(generations[y])
+        cheapest_chromosome = generations[y]
+        lowest_cost = first_cost
+        highest_cost = first_cost
+        for i in generations:
+            chromosome_costs.append(self.calc_cost(i))
+            total_cost = total_cost + self.calc_cost(i)
+            chromosome_count = chromosome_count + 1
+            if self.calc_cost(i) < lowest_cost:
+                lowest_cost = self.calc_cost(i)
+                cheapest_chromosome = i
+            if self.calc_cost(i) > highest_cost:
+                highest_cost = self.calc_cost(i)
+        
+        average_fitness = total_cost/chromosome_count
+        chromosome_fitness = []
+        total_chromosome_fitness = 0
+        l = 0
+        for i in chromosome_costs:
+            chromosome_fitness.append(round(((average_fitness/chromosome_costs[l])*10), 2))
+            total_chromosome_fitness += round(((average_fitness/chromosome_costs[l])*10),2)
+            l = l + 1
+
+        return(chromosome_fitness, lowest_cost, highest_cost, average_fitness, cheapest_chromosome)
+
+
+    def fitness_population_selection(self, first_generation, chromosome_fitness):
+        x = len(chromosome_fitness)
+        roulette_table = []
+        average_population = []
+        i = 0
+        interval = 0
+        while x > 0:
+            interval = interval + chromosome_fitness[i]
+            roulette_table.append(round(interval, 2))
+            x = x - 1
+            i = i + 1
+        x = len(chromosome_fitness)/2
+        max = roulette_table[i - 1]
+        while x > 0:
+            i = 0
+            n = random.uniform(0, max)
+            while n > roulette_table[i]:
+                i = i + 1
+            average_population.append(first_generation[i])
+            x = x - 1
+        return average_population
+
+    def crossover(self, average_population):
+        post_crossover_population = []
+        x = len(average_population) - 1
+        if x <= 0:
+            print("no enough chromosomes to crossover")
+            return
+        while x > 0:
+            parent_1 = average_population[x]
+            parent_2 = average_population[x-1]
+            child_1 = []
+            child_2 = []
+            crossover_decision = random.randint(1, 100)
+            if (crossover_decision < 11) and (parent_1 != parent_2):
+                crossover_point = random.randint(1, (len(average_population[x])-3))
+                l = 0 
+                k = crossover_point
+                while k >= 0:
+                    child_1.append(parent_1[l])
+                    l = l + 1
+                    k = k - 1
+                k = crossover_point
+                while k < (len(parent_1) - 1):
+                    for i in parent_2:
+                        if i not in child_1:
+                            child_1.append(i)
+                        k = k + 1
+                
+                l = 0
+                k = crossover_point
+                while k >= 0:
+                    child_2.append(parent_2[l])
+                    l = l + 1
+                    k = k - 1
+                k = crossover_point
+                while k >= 0:
+                    child_2.append(parent_2[l])
+                    l = l + 1
+                    k = k - 1
+                k = crossover_point
+                while k < (len(parent_1) - 1):
+                    for i in parent_1:
+                        if i not in child_2:
+                            child_2.append(i)
+                        k = k + 1
+                #child_1.append(0)
+                #child_2.append(0)
+            else:
+                child_1 = parent_1
+                child_2 = parent_2
+            post_crossover_population.append(child_1)
+            post_crossover_population.append(child_2)
+            x = x - 1
+
+        return post_crossover_population
+
+    def mutation(self, post_crossover_popualtion):
+        k = len(post_crossover_popualtion) - 1
+        while k >= 0:
+            mutation_decision = random.randint(0, 100)
+            if mutation_decision < 3:
+                mutated_chromosome = post_crossover_popualtion[k]
+                post_crossover_popualtion.remove(mutated_chromosome)
+                l = len(mutated_chromosome) - 1
+
+                x = random.randint(1, l)
+                y = random.randint(1, l)
+                while x == y:
+                    y = random.randint(1, l)
+                replacement = mutated_chromosome[x]
+                mutated_chromosome[y] = replacement
+
+                post_crossover_popualtion.insert(k, mutated_chromosome)
+        
+            k = k - 1
+        post_mutation_population = post_crossover_popualtion
+
+        return post_mutation_population
+
+    def optimize(self, post_mutation_population, mine_list):
+        post_optimization_population = post_mutation_population
+        i = len(post_mutation_population)
+        l = 1
+        while l < i:
+            k = 1
+            while k >= 0:
+                if post_mutation_population[l] == post_mutation_population[k - 1]:
+                    post_optimization_population.remove(post_mutation_population[k-1])
+                    x = len(mine_list) - 2
+                    shuffled_coordinates = []
+                    while x > 0:
+                        shuffled_coordinates.append(mine_list[x])
+                        x = x - 1
+                    x = len(mine_list) - 1
+                    new_chromosome = random.sample(shuffled_coordinates, len(shuffled_coordinates))
+                    new_chromosome.insert(0, mine_list[0])
+                    new_chromosome.insert(x, mine_list[0])
+                    post_optimization_population.append(new_chromosome)
+                
+                k = k - 1
+            l = l + 1
+        return post_optimization_population
+
+    def genetic_algorythm(self, coordinates):
+        self.full_cost = self.calc_cost(coordinates)
+        self.first_generation = self.population_initialization(coordinates,10)
+        self.fitness, self.lowest_cost, self.highest_cost, self.average_fitness_of_first_genetation, self.cheapest_individual = self.fitness_function(self.first_generation)
+        self.average_population = self.fitness_population_selection(self.first_generation, self.fitness)
+        self.population_after_crossover = self.crossover(self.average_population)
+
+
+        if self.population_after_crossover == None:
+            print("Finished in " + str(self.which_generation) + "generations")
+            return
+
+        self.population_after_mutation = self.mutation(self.population_after_crossover)
+        self.population_after_optimization = self.optimize(self.population_after_mutation, coordinates)
+        self.max_cost = self.highest_cost
+        self.min_cost = self.lowest_cost
+        self.cheapest_route = self.cheapest_individual
+        i = 2
+        self.which_generation = 1
+        while i < 41:
+            print(" ")
+            print("***********")
+            print("Generation " + str(i))
+            print("***********")
+            print(" ")
+
+            if self.fitness_function(self.population_after_optimization) == None:
+                return
+
+            self.fitness, self.lowest_cost, self.highest_cost, self.average_fitness_of_first_genetation, self.cheapest_individual = self.fitness_function(self.population_after_optimization)
+            if self.highest_cost > self.max_cost:
+                self.max_cost = self.highest_cost
+            if self.lowest_cost < self.min_cost:
+                self.min_cost = self.lowest_cost
+                self.cheapest_route = self.cheapest_individual
+                self.which_generation = i
+                print("New lowest cost: " + str(self.min_cost))
+                print("New cheapest route: " + str(self.cheapest_route))
+            
+            self.average_population = self.fitness_population_selection(self.population_after_mutation, self.fitness)
+
+            self.population_after_crossover = self.crossover(self.average_population)
+
+            if self.population_after_crossover == None:
+                print("Finished")
+                return
+
+            self.population_after_mutation = self.mutation(self.population_after_crossover)
+            self.population_after_optimization = self.optimize(self.population_after_mutation, coordinates)
+            i = i + 1
+            if(self.min_cost)/(self.average_fitness_of_first_genetation) < (0.7):
+                print("Finished after " + str(i)+ " generations")
+                break
+            print("Average fitness of first generation: " + str(self.average_fitness_of_first_genetation))
+            print("Relation of efficiency growth: " + str((self.min_cost)/(self.average_fitness_of_first_genetation)))
+            print("Lowest cost is " + str(self.min_cost))
+            print("Cheapest route is: " + str(self.cheapest_route))
+    
+    def run(self):
+        Mines = [[1,1],
+                 [2,4],
+                 [3,1],
+                 [4,7],
+                 [5,3],
+                 [7,6]]
+        
+        self.genetic_algorythm(Mines)
+
+
+
+
+
+            
\ No newline at end of file
diff --git a/genetic3.py b/genetic3.py
new file mode 100644
index 0000000..6155890
--- /dev/null
+++ b/genetic3.py
@@ -0,0 +1,106 @@
+import numpy as np
+import math
+import random
+from geneticalgorithm import geneticalgorithm as ga
+from astar2 import *
+
+
+class Mine():
+    def __init__(self, row, column):
+        self.row = row
+        self.columnt = column
+
+    
+
+
+
+class GeneticalWithLib():
+
+    Miasta = [[0,   130,     180,   300],
+            [130, 0,       320,   350],
+            [180, 320,     0,     360],
+            [300, 350,     360,   0]]
+    #Miasta =   [[0, 2, 6, 3, 7],
+    #            [2, 0, 6, 4, 8],
+    #            [6, 6, 0, 5, 8],
+    #            [3, 4, 5, 0, 9],
+    #            [7, 8, 8, 9, 0]]
+
+    self.mine_points = set()
+
+
+    def getCoordinates(self):
+        temp_map = [list(item) for item in SweeperAgent.loadMap('genetic_maps/map1.txt')]
+
+        a_row = 0
+        a_column = 0
+
+        for row in range(MAP_SIZE):
+            for column, pos in enumerate(temp_map[row]):
+                if pos == "m" :
+                    a_row = row
+                    a_column = column
+                    location = tuple([a_row, a_column])
+                    self.mine_points.add(location)
+
+    def setDistance(self):
+        pass
+
+
+    def fillDistanceMatrix(self):
+        pass
+
+
+
+    def f(X):
+        for i in range(len(Miasta[0])):
+            if X[i] not in Miasta[i]:
+                return np.sum(X)+12000
+        for i in range(len(Miasta[0])):
+            if i == 0:
+                if (X[0]-X[len(Miasta[0])-1]) == 0:
+                    return np.sum(X)+12000
+            else:
+                if abs(X[i]-X[i-1]) == 0:
+                    return np.sum(X)+12000
+                if i != len(Miasta[0])-1:
+                    if abs(X[i+1]-X[i-1]) == 0:
+                        return np.sum(X)+12000
+        var = []
+        for j in range(len(Miasta[0])):
+            var.append(0)
+        for i in range(len(Miasta[0])):
+            var[Miasta[i].index(X[i])] += 1
+        for s in var:
+            if s != 1:
+                return np.sum(X)+9000
+        return np.sum(X)
+
+    varbound = np.array([[130,300],
+                        [130,350],
+                        [180,360],
+                        [300,360]])
+
+    #varbound = np.array([[2,7],
+    #                    [2,8],
+    #                    [5,8],
+    #                    [4,9],
+    #                    [7,9]])
+
+    algorithm_param = {'max_num_iteration': 3000,\
+                    'population_size':100,\
+                    'mutation_probability':0.1,\
+                    'elit_ratio': 0.01,\
+                    'crossover_probability': 0.5,\
+                    'parents_portion': 0.3,\
+                    'crossover_type':'uniform',\
+                    'max_iteration_without_improv':None}
+
+    model=ga(function=f,\
+                dimension=len(Miasta[0]),\
+                variable_type='int',\
+                variable_boundaries=varbound,\
+                algorithm_parameters=algorithm_param)
+
+    model.run()
+    #print(f([180, 350, 180, 350]))
\ No newline at end of file
diff --git a/genetic_maps/map1.txt b/genetic_maps/map1.txt
new file mode 100644
index 0000000..9eb11d0
--- /dev/null
+++ b/genetic_maps/map1.txt
@@ -0,0 +1,8 @@
+x......
+...x...
+x......
+......x
+..x....
+.......
+.....x.
+
diff --git a/main.py b/main.py
index 86ae369..21ebbe5 100644
--- a/main.py
+++ b/main.py
@@ -17,6 +17,8 @@ from astar2 import *
 from pizza import *
 from learning import *
 from nn import *
+from genetic import *
+#from genetic3 import *
 #from csv_writer import * 
 
 
@@ -153,6 +155,16 @@ class Game:
                     player_moves = SweeperAgent.run(agent)
                     self.graph_move(player_moves)
                     self.wentyl_bezpieczenstwa = 1
+                if event.key == pg.K_F8 and self.wentyl_bezpieczenstwa == 0:
+                    #print("pressed")
+                    genetic_runner = Genetic()
+                    genetic_runner.run()
+                    self.wentyl_bezpieczenstwa = 1
+                if event.key == pg.K_F9 and self.wentyl_bezpieczenstwa == 0:
+                    #genetic_lib_runner = GeneticalWithLib()
+                    #genetic_lib_runner.run()
+                    self.wentyl_bezpieczenstwa = 1
+