diff --git a/AI/GeneticAlgorithm.py b/AI/GeneticAlgorithm.py
index 5f1d62d..bf9d972 100644
--- a/AI/GeneticAlgorithm.py
+++ b/AI/GeneticAlgorithm.py
@@ -43,8 +43,8 @@ def genetic_algorithm_setup(field):
 
     # iterative var
     generation = 0
-
-    while generation < num_generations:
+    # TODO WARUNEK STOPU
+    while generation < num_generations and stop < 3:
         if keyboard.is_pressed('space'):
             generation += 1
 
@@ -82,6 +82,7 @@ def genetic_algorithm_setup(field):
             offspring_x = random.randint(1, D.GSIZE - 2)
             offspring_y = random.randint(1, D.GSIZE - 2)
 
+            # TODO OPERATOR KRZYŻOWANIA
             offspring_crossover = crossover(parents)
             print("Crossover")
             for i in range(0, len(offspring_crossover)):
@@ -89,7 +90,7 @@ def genetic_algorithm_setup(field):
                                  for row in offspring_crossover[i]]))
                 print("")
 
-            # Adding some variations to the offspring using mutation.
+            # TODO OPERATOR MUTACJI
             offspring_mutation = mutation(population_units, offspring_crossover, population_size - num_parents,
                                           num_mutations=10)
             print("Mutation")
@@ -105,6 +106,16 @@ def genetic_algorithm_setup(field):
             for k in range(0, len(offspring_mutation)):
                 population_text.append(offspring_mutation[k])
 
+            # TODO WARUNEK STOPU
+            stop = 0
+            if generation > 3:
+                if best_outputs[-1] / best_outputs[-2] < 1.05:
+                    stop += 1
+                if best_outputs[-1] / best_outputs[-3] < 1.05:
+                    stop += 1
+                if best_outputs[-2] / best_outputs[-3] < 1.05:
+                    stop += 1
+
     # final Fitness
     fitness = []
     for i in range(0, population_size):