diff --git a/src/memetic_algorithm.py b/src/memetic_algorithm.py
index 2f25fcc..9a07df0 100644
--- a/src/memetic_algorithm.py
+++ b/src/memetic_algorithm.py
@@ -13,21 +13,34 @@ def get_best_indices(n, population):
     return best_elements
 
 
+def replace_elements(current_population, new_population, indices):
+    for item in indices:
+        current_population[item] = new_population[item]
+    return current_population
+
+
 def run_local_search(n, data, population, mode, probability=0.1):
-    new_population = []
+    neighbourhood = []
     if mode == "all":
         for individual in population:
-            new_population.append(local_search(individual, n, data))
+            neighbourhood.append(local_search(individual, n, data))
+        new_population = neighbourhood
     elif mode == "random":
         expected_individuals = len(population) * probability
+        indices = []
         for _ in range(expected_individuals):
-            random_individual = population[randint(len(population))]
-            new_population.append(local_search(random_individual, n, data))
+            random_index = randint(len(population))
+            random_individual = population[random_index]
+            neighbourhood.append(local_search(random_individual, n, data))
+            indices.append(random_index)
+        new_population = replace_elements(population, neighbourhood, indices)
     else:
         expected_individuals = len(population) * probability
-        best_indexes = get_best_indices(n=expected_individuals, population=population)
-        for element in best_indexes:
-            new_population.append(local_search(population[element], n, data))
+        best_indices = get_best_indices(n=expected_individuals, population=population)
+        for element in best_indices:
+            neighbourhood.append(local_search(population[element], n, data))
+        new_population = replace_elements(population, neighbourhood, best_indices)
+    return new_population
 
 
 def memetic_algorithm(n, m, data, hybridation, max_iterations=100000):
@@ -36,7 +49,7 @@ def memetic_algorithm(n, m, data, hybridation, max_iterations=100000):
     for i in range(max_iterations):
         if i % 10 == 0:
             best_index, _ = get_best_elements(population)
-            run_local_search(n, data, population, mode=hybridation)
+            population = run_local_search(n, data, population, mode=hybridation)
             i += 5
         parents = select_parents(population, n, mode="stationary")
         offspring = crossover(mode="position", parents=parents, m=m)