diff --git a/src/genetic_algorithm.py b/src/genetic_algorithm.py
index dedba02..cae5eb1 100644
--- a/src/genetic_algorithm.py
+++ b/src/genetic_algorithm.py
@@ -49,10 +49,11 @@ def evaluate_individual(individual, data):
 
 
 def select_distinct_genes(matching_genes, parents, m):
-    cutoff = randint(m)
-    distinct_indexes = delete(arange(m), matching_genes)
-    first_parent_genes = parents[0].point.iloc[distinct_indexes[cutoff:]]
-    second_parent_genes = parents[1].point.iloc[distinct_indexes[:cutoff]]
+    first_parent = parents[0].query("point not in @matching_genes")
+    second_parent = parents[1].query("point not in @matching_genes")
+    cutoff = randint(len(first_parent.point.values))
+    first_parent_genes = first_parent.point.values[cutoff:]
+    second_parent_genes = second_parent.point.values[:cutoff]
     return first_parent_genes, second_parent_genes
 
 
@@ -67,23 +68,26 @@ def select_random_genes(matching_genes, parents, m):
 def repair_offspring(offspring, parents, m):
     while len(offspring) != m:
         if len(offspring) > m:
-            best_index = offspring["distance"].astype(float).idxmax()
+            best_index = offspring["distance"].idxmax()
             offspring.drop(index=best_index, inplace=True)
         elif len(offspring) < m:
             random_parent = parents[randint(len(parents))]
-            best_index = random_parent["distance"].astype(float).idxmax()
-            best_point = random_parent["point"].loc[best_index]
+            while True:
+                best_index = random_parent["distance"].idxmax()
+                best_point = random_parent["point"].loc[best_index]
+                random_parent.drop(index=best_index, inplace=True)
+                if not any(offspring["point"].isin([best_point])):
+                    break
             offspring = offspring.append(
-                {"point": best_point, "distance": 0}, ignore_index=True
+                {"point": best_point, "distance": 0, "fitness": 0}, ignore_index=True
             )
-            random_parent.drop(index=best_index, inplace=True)
     return offspring
 
 
 def get_matching_genes(parents):
-    first_parent = parents[0].point
-    second_parent = parents[1].point
-    return where(first_parent == second_parent)
+    first_parent = parents[0].point.values
+    second_parent = parents[1].point.values
+    return where(first_parent == second_parent)[0]
 
 
 def populate_offspring(values):
@@ -99,8 +103,7 @@ def populate_offspring(values):
 
 
 def uniform_crossover(parents, m):
-    matching_indexes = get_matching_genes(parents)
-    matching_genes = parents[0].point.iloc[matching_indexes]
+    matching_genes = get_matching_genes(parents)
     first_genes, second_genes = select_distinct_genes(matching_genes, parents, m)
     offspring = populate_offspring(values=[matching_genes, first_genes, second_genes])
     viable_offspring = repair_offspring(offspring, parents, m)
@@ -116,9 +119,13 @@ def position_crossover(parents, m):
 
 
 def crossover(mode, parents, m):
+    split_parents = [parents[i : i + 2] for i in range(0, len(parents), 2)]
     if mode == "uniform":
-        return uniform_crossover(parents, m)
-    return position_crossover(parents, m)
+        crossover_func = partial(uniform_crossover, m=m)
+    else:
+        crossover_func = partial(position_crossover, m=m)
+    offspring = [*map(crossover_func, split_parents)]
+    return offspring
 
 
 def element_in_dataframe(individual, element):