diff --git a/src/genetic_algorithm.py b/src/genetic_algorithm.py
index c305ccd..54a1b03 100644
--- a/src/genetic_algorithm.py
+++ b/src/genetic_algorithm.py
@@ -5,6 +5,7 @@ from math import ceil
 from functools import partial
 from multiprocessing import Pool
 from copy import deepcopy
+from itertools import chain, repeat
 
 from preprocessing import parse_file
 
@@ -130,12 +131,15 @@ def position_crossover(parents, m):
 
 
 def crossover(mode, parents, m):
-    split_parents = zip(*[iter(parents)] * 2)
+    split_parents = list(zip(*[iter(parents)] * 2))
+    offspring = []
     if mode == "uniform":
-        crossover_func = partial(uniform_crossover, m=m)
+        for i in range(len(split_parents)):
+            offspring.append(uniform_crossover(split_parents[i], m))
+            offspring.append(uniform_crossover(split_parents[i], m))
     else:
         crossover_func = partial(position_crossover, m=m)
-    offspring = [*map(crossover_func, split_parents)]
+        offspring = [*map(crossover_func, split_parents)]
     return offspring
 
 
@@ -211,18 +215,26 @@ def get_best_elements(population):
 
 
 def get_worst_elements(population):
-    first_index = population.index(min(population, key=lambda x: all(x.fitness)))
-    population.pop(first_index)
-    second_index = population.index(min(population, key=lambda x: all(x.fitness)))
+    select_population = deepcopy(population)
+    first_element = min(select_population, key=lambda x: x.fitness.values[0])
+    first_index = get_individual_index(first_element, select_population)
+    select_population.pop(first_index)
+    second_element = min(select_population, key=lambda x: x.fitness.values[0])
+    second_index = get_individual_index(second_element, select_population)
     return first_index, second_index
 
 
 def stationary_replacement(prev_population, current_population):
     new_population = prev_population
-    worst_indexes = get_worst_elements(prev_population)
-    best_indexes = get_best_elements(current_population)
+    first_worst, second_worst = get_worst_elements(prev_population)
+    first_best, second_best = get_best_elements(current_population)
+    worst_indexes = [first_worst, second_worst]
+    best_indexes = [first_best, second_best]
     for worst, best in zip(worst_indexes, best_indexes):
-        if current_population[best].fitness > prev_population[worst].fitness:
+        if (
+            current_population[best].fitness.values[0]
+            > prev_population[worst].fitness.values[0]
+        ):
             new_population[worst] = current_population[best]
     return new_population
 
@@ -276,5 +288,5 @@ genetic_algorithm(
     data=data,
     select_mode="generational",
     crossover_mode="uniform",
-    max_iterations=1,
+    max_iterations=10,
 )