diff --git a/genetic-algorithm.py b/genetic-algorithm.py
index 928211f..28f9bc9 100644
--- a/genetic-algorithm.py
+++ b/genetic-algorithm.py
@@ -1,7 +1,9 @@
 #/bin/python
 import random
 
-tasks = [4, 5, 8, 2, 10, 7]
+# tasks = [4, 5, 8, 2, 10, 7]
+# tasks = [random.randint(1,20) for _ in range(100)]
+tasks = [19, 12, 12, 3, 5, 10, 2, 17, 18, 8, 20, 6, 3, 10, 6, 1, 13, 16, 17, 1, 11, 6, 12, 2, 11, 17, 8, 12, 7, 15, 1, 5, 17, 19, 5, 16, 15, 20, 7, 4, 12, 6, 17, 6, 8, 10, 8, 13, 15, 2, 6, 16, 11, 16, 16, 16, 1, 2, 17, 9, 4, 3, 7, 4, 1, 14, 16, 1, 1, 15, 20, 2, 13, 15, 15, 11, 5, 18, 1, 14, 19, 19, 19, 19, 5, 5, 12, 5, 2, 2, 2, 9, 8, 12, 6, 20, 18, 12, 12, 19]
 sol1=[1, 0, 1, 1, 0, 1]
 sol2=[0, 0, 1, 0, 1, 1]
 
@@ -27,11 +29,12 @@ def genetic(problem_data, k, popsize, mutation_chance, stop, reproduce):
         best = fitness(population[-1], problem_data, k)
         new = []
         for _ in range(reproduce):
-            parents = random.choices(population, cum_weights=[1]*popsize, k=2)
+            weightsP = [(i+1)**2 for i in range(popsize)]
+            parents = random.choices(population, weights=weightsP, k=2)
             x = random.random()
             offspring = crossover(*parents)
             new.append(mutation(offspring, k) if mutation_chance>x else offspring)
-        population = sorted(population + new, key=lambda x: fitness(x, problem_data, k), reverse=True)[-4:]
+        population = sorted(population + new, key=lambda x: fitness(x, problem_data, k), reverse=True)[-popsize:]
 
         new_best = fitness(population[-1], problem_data, k)
         loop = loop + 1 if best<=new_best else 0
@@ -39,6 +42,6 @@ def genetic(problem_data, k, popsize, mutation_chance, stop, reproduce):
 
     return(population[-1])
 
-ans = genetic(tasks, 2, 4, 0.2, 10, 4)
+ans = genetic(tasks, 4, 10, 0.7, 10, 5)
 print(ans)
-print(fitness(ans, tasks, 2))
+print(fitness(ans, tasks, 4))