diff --git a/genetic_algorithm.py b/genetic_algorithm.py
index 1918db8..b2ef21f 100644
--- a/genetic_algorithm.py
+++ b/genetic_algorithm.py
@@ -1,18 +1,16 @@
 import random
 import math
-import statistics
-import time
 
 ### prawdopodobieństwo mutacji
-mutation_prob = 0.02
+mutation_prob = 0.03
 ### ilość osobników w pokoleniu, powinna być parzysta
-generation_size = 30
+generation_size = 20
 ### liczba pokoleń
-number_of_generations = 100
+number_of_generations = 30
 ### liczba paczek
-number_of_packages = 15
+number_of_packages = 45
 ### liczba regałów
-number_of_racks = 5
+number_of_racks = 70
 ### jak bardzo promowane są osobniki wykorzystujące całą pojemność regału
 amount_of_promotion = 3
 
@@ -95,55 +93,58 @@ def mutation(individual):
     individual[locus] = random.randint(0,number_of_racks-1)
     return individual
 
+
+def gen_alg(number_of_generations, generation_size, mutation_prob, amount_of_promotion):
+    ### WŁAŚCIWY ALGORYTM
+    generation = first_gen()
+    global_maximum = -math.inf
+
+    # pętla znajdująca najlepszy fitness w pierwszym pokoleniu
+    for i in range(generation_size):
+        evaluation_of_individual = evaluation(generation[i])
+        if evaluation_of_individual > global_maximum:
+            global_maximum = evaluation_of_individual
+            best_individual = generation[i].copy()
+
+    #właściwa pętla programu
+    for generation_index in range(number_of_generations):
+        print('pokolenie numer: ', generation_index)
+        # print(generation)
+
+        ### RULETKA
+        survivors = roulette(generation)
+        # print('przetrwali: ',survivors)
+
+        ### KRZYŻOWANIE
+        descendants = []
+        for individual in range(0,generation_size,2):
+            pair = crossover(survivors[individual],survivors[individual+1])
+            for each in pair:
+                descendants.append(each)
+        # print('potomkowie: ', descendants)
+
+        ### MUTACJA
+        for individual in range(generation_size):
+            if random.random() <= mutation_prob:
+                mutation(descendants[individual])
+        # print('potomkowie po mutacji: ', descendants)
+
+        ### NAJLEPSZE DOPASOWANIE
+        local_maximum = -math.inf
+        for each in range(generation_size):
+            specific_fitness = evaluation(descendants[each])
+            if specific_fitness > local_maximum:
+                local_maximum = specific_fitness
+        print('maximum w pokoleniu: ',local_maximum)
+        if local_maximum > global_maximum:
+            global_maximum = local_maximum
+        generation = descendants
+    print('maximum globalne: ', global_maximum)
+
 ### lista paczek, indeks to id paczki, wartość w liście to jej waga
-packages = [random.randint(2,9) for i in range(number_of_packages)]
+packages = [random.randint(1,10) for i in range(number_of_packages)]
 ### lista regałów, indeks to id regału, wartość w liście to jego pojemność
-racks = [random.randint(10,20) for i in range(number_of_racks)]
+racks = [random.randint(15,18) for i in range(number_of_racks)]
 # print(packages)
 # print(racks)
-
-### WŁAŚCIWY ALGORYTM
-generation = first_gen()
-global_maximum = -math.inf
-
-# pętla znajdująca najlepszy fitness w pierwszym pokoleniu
-for i in range(generation_size):
-    evaluation_of_individual = evaluation(generation[i])
-    if evaluation_of_individual > global_maximum:
-        global_maximum = evaluation_of_individual
-        best_individual = generation[i].copy()
-
-#właściwa pętla programu
-for generation_index in range(number_of_generations):
-    print('pokolenie numer: ', generation_index)
-    # print(generation)
-
-    ### RULETKA
-    survivors = roulette(generation)
-    # print('przetrwali: ',survivors)
-
-    ### KRZYŻOWANIE
-    descendants = []
-    for individual in range(0,generation_size,2):
-        pair = crossover(survivors[individual],survivors[individual+1])
-        for each in pair:
-            descendants.append(each)
-    # print('potomkowie: ', descendants)
-
-    ### MUTACJA
-    for individual in range(generation_size):
-        if random.random() <= mutation_prob:
-            mutation(descendants[individual])
-    # print('potomkowie po mutacji: ', descendants)
-
-    ### NAJLEPSZE DOPASOWANIE
-    local_maximum = -math.inf
-    for each in range(generation_size):
-        specific_fitness = evaluation(descendants[each])
-        if specific_fitness > local_maximum:
-            local_maximum = specific_fitness
-    print('maximum w pokoleniu: ',local_maximum)
-    if local_maximum > global_maximum:
-        global_maximum = local_maximum
-    generation = descendants
-print('maximum globalne: ', global_maximum)
+gen_alg(number_of_generations, generation_size, mutation_prob, amount_of_promotion)