diff --git a/AI/GeneticAlgorithm.py b/AI/GeneticAlgorithm.py
index 5d45d24..7b9ad29 100644
--- a/AI/GeneticAlgorithm.py
+++ b/AI/GeneticAlgorithm.py
@@ -32,7 +32,7 @@ def genetic_algorithm_setup(field):
 
     # units per population in generation
     best_outputs = []
-    num_generations = 10
+    num_generations = 100
     num_parents = 4
 
     # iterative var
@@ -65,14 +65,13 @@ def genetic_algorithm_setup(field):
 
             # TODO METODA WYBORU OSOBNIKA - RANKING
             # Selecting the best parents in the population for mating.
-            print(best)
             parents = [population_text[i[0]] for i in best]
             parents_copy = copy.deepcopy(parents)
             print("Parents")
-            # for i in range(0, len(parents)):
-            #     print('\n'.join([''.join(['{:4}'.format(item) for item in row])
-            #                      for row in parents[i]]))
-            #     print("")
+            for i in range(0, len(parents)):
+                print('\n'.join([''.join(['{:4}'.format(item) for item in row])
+                                 for row in parents[i]]))
+                print("")
 
             # Generating next generation using crossover.
             offspring_x = random.randint(1, D.GSIZE - 2)
@@ -81,19 +80,19 @@ def genetic_algorithm_setup(field):
             # TODO OPERATOR KRZYŻOWANIA
             offspring_crossover = crossover(parents)
             print("Crossover")
-            # for i in range(0, len(offspring_crossover)):
-            #     print('\n'.join([''.join(['{:4}'.format(item) for item in row])
-            #                      for row in offspring_crossover[i]]))
-            #     print("")
+            for i in range(0, len(offspring_crossover)):
+                print('\n'.join([''.join(['{:4}'.format(item) for item in row])
+                                 for row in offspring_crossover[i]]))
+                print("")
 
             # TODO OPERATOR MUTACJI
             offspring_mutation = mutation(population_units, offspring_crossover, population_size - num_parents,
                                           num_mutations=10)
             print("Mutation")
-            # for i in range(0, len(offspring_mutation)):
-            #     print('\n'.join([''.join(['{:4}'.format(item) for item in row])
-            #                      for row in offspring_mutation[i]]))
-            #     print("")
+            for i in range(0, len(offspring_mutation)):
+                print('\n'.join([''.join(['{:4}'.format(item) for item in row])
+                                 for row in offspring_mutation[i]]))
+                print("")
 
             population_text_copy = copy.deepcopy(population_text)
             unused_indexes = [i for i in range(0, population_size) if i not in [j[0] for j in best]]
@@ -111,11 +110,11 @@ def genetic_algorithm_setup(field):
             # TODO WARUNEK STOPU
             stop = 0
             if generation > 10:
-                if best_outputs[-1] / best_outputs[-2] < 1.05:
+                if best_outputs[-1] / best_outputs[-2] < 1.001:
                     stop += 1
-                if best_outputs[-1] / best_outputs[-3] < 1.05:
+                if best_outputs[-1] / best_outputs[-3] < 1.001:
                     stop += 1
-                if best_outputs[-2] / best_outputs[-3] < 1.05:
+                if best_outputs[-2] / best_outputs[-3] < 1.001:
                     stop += 1
 
     # final Fitness