diff --git a/genetic_algorithm.py b/genetic_algorithm.py
index a7d7f69..dc12f9e 100755
--- a/genetic_algorithm.py
+++ b/genetic_algorithm.py
@@ -3,14 +3,14 @@ import math
 ### prawdopodobieństwo mutacji
 mutation_prob = 0.03
 ### ilość osobników w pokoleniu, powinna być parzysta
-generation_size = 40
+# generation_size = 40
 ### liczba pokoleń
-number_of_generations = 30
+# number_of_generations = 30
 ### jak bardzo promowane są osobniki wykorzystujące całą pojemność regału
 amount_of_promotion = 5
 
 
-def first_gen(number_of_packages, number_of_racks):
+def first_gen(number_of_packages, number_of_racks, generation_size):
     first_generation = []
     for individual in range(generation_size):
         individual = []
@@ -20,17 +20,43 @@ def first_gen(number_of_packages, number_of_racks):
         first_generation.append(individual)
     return first_generation
 
+# def evaluation(individual, packages, racks, number_of_packages, number_of_racks, tree_predictor):
+#     # im większy fitness tym lepszy osobnik
+#     # print("regały: ",racks)
+#     rest_of_capacity = [rack.capacity for rack in racks]
+#     # print("początkowa pojemność: ",rest_of_capacity)
+#     for i in range(number_of_packages):
+#         can_place = tree_predictor.check_if_can_place(packages[i], racks[i])
+#         if not can_place:
+#             rest_of_capacity[individual[i]] -= packages[i].size * 5
+#         else:
+#             rest_of_capacity[individual[i]] -= packages[i].size
+#     # print("pozostała pojemność: ",rest_of_capacity)
+#     # pdb.set_trace()
+#     fitness = 0
+#     for i in range(number_of_racks):
+#         # jak regał jest przepełniony, zmniejsza fitness osobnika
+#         if rest_of_capacity[i] < 0:
+#             fitness += rest_of_capacity[i]
+#             # delikane promowanie osobników wykorzystujących regały w pełni
+#         elif rest_of_capacity[i] == 0:
+#             fitness += 2
+#         else:
+#             fitness += 1
+#     return fitness
+
+
 def evaluation(individual, packages, racks, number_of_packages, number_of_racks, tree_predictor):
     # im większy fitness tym lepszy osobnik
     # print("regały: ",racks)
     rest_of_capacity = [rack.capacity for rack in racks]
     # print("początkowa pojemność: ",rest_of_capacity)
+    bad_placed = 0
     for i in range(number_of_packages):
+        rest_of_capacity[individual[i]] -= packages[i].size
         can_place = tree_predictor.check_if_can_place(packages[i], racks[i])
         if not can_place:
-            rest_of_capacity[individual[i]] -= packages[i].size * 5
-        else:
-            rest_of_capacity[individual[i]] -= packages[i].size
+            bad_placed +=1
     # print("pozostała pojemność: ",rest_of_capacity)
     # pdb.set_trace()
     fitness = 0
@@ -40,12 +66,11 @@ def evaluation(individual, packages, racks, number_of_packages, number_of_racks,
             fitness += rest_of_capacity[i]
             # delikane promowanie osobników wykorzystujących regały w pełni
         elif rest_of_capacity[i] == 0:
-            fitness += 2
-        else:
-            fitness += 1
+            fitness += amount_of_promotion
+    fitness -= 5*bad_placed
     return fitness
 
-def roulette(generation, packages, racks, number_of_packages, number_of_racks, tree_predictor):
+def roulette(generation, packages, generation_size ,racks, number_of_packages, number_of_racks, tree_predictor):
     # print('pokolenie: ', generation)
     evaluations = []
     for i in range(generation_size):
@@ -100,7 +125,7 @@ def gen_alg(packages, racks, number_of_generations, generation_size, mutation_pr
     number_of_racks = len(racks)
 
     ### WŁAŚCIWY ALGORYTM
-    generation = first_gen(number_of_packages, number_of_racks)
+    generation = first_gen(number_of_packages, number_of_racks, generation_size)
     global_maximum = -math.inf
 
     # pętla znajdująca najlepszy fitness w pierwszym pokoleniu
@@ -116,7 +141,7 @@ def gen_alg(packages, racks, number_of_generations, generation_size, mutation_pr
         # print(generation)
 
         ### RULETKA
-        survivors = roulette(generation, packages, racks, number_of_packages, number_of_racks, tree_predictor)
+        survivors = roulette(generation, packages, generation_size, racks, number_of_packages, number_of_racks, tree_predictor)
         # print('przetrwali: ',survivors)
 
         ### KRZYŻOWANIE
diff --git a/main.py b/main.py
index 79cbcab..ae00e2c 100755
--- a/main.py
+++ b/main.py
@@ -20,7 +20,7 @@ TILE_HEIGHT = 32
 CIRCLE_CENTER_X, CIRCLE_CENTER_Y = int(TILE_WIDTH/2), int(TILE_HEIGHT/2)
 
 class MainGameFrame:
-    def __init__(self, mutation_prob=0.03, generation_size=40, number_of_generations=100, amount_of_promotion=0):
+    def __init__(self, mutation_prob=0.05, generation_size=30, number_of_generations=100, amount_of_promotion=0):
         pygame.font.init()
         self.display = pygame.display.set_mode(WINDOW_SIZE)
         pygame.display.set_caption("Smart ForkLift")