diff --git a/src/genetic_algorithm.py b/src/genetic_algorithm.py
index 6ce7ffd..fe0dd47 100644
--- a/src/genetic_algorithm.py
+++ b/src/genetic_algorithm.py
@@ -2,6 +2,10 @@ from numpy import sum, append, arange, delete, where
 from numpy.random import randint, choice, shuffle
 from pandas import DataFrame
 from math import ceil
+from functools import partial
+from multiprocessing import Pool
+
+from preprocessing import parse_file
 
 
 def get_row_distance(source, destination, data):
@@ -40,7 +44,8 @@ def evaluate_individual(individual, data):
         max_distance = element_df["distance"].astype(float).max()
         fitness = append(arr=fitness, values=max_distance)
         distances = distances.query(f"source != {item} and destination != {item}")
-    return sum(fitness)
+    individual["fitness"] = sum(fitness)
+    return individual
 
 
 def select_distinct_genes(matching_genes, parents, m):
@@ -188,5 +193,19 @@ def replace_population(prev_population, current_population, mode):
     return stationary_replacement(prev_population, current_population)
 
 
-def genetic_algorithm(n, m, data, mode):
+def evaluate_population(population, data, cores=4):
+    fitness_func = partial(evaluate_individual, data=data)
+    with Pool(cores) as pool:
+        evaluated_population = pool.map(fitness_func, population)
+    return evaluated_population
+
+
+def genetic_algorithm(n, m, data, mode, max_iterations=100000):
     population = [generate_individual(n, m, data) for _ in range(n)]
+    population = evaluate_population(population, data)
+    for _ in range(max_iterations):
+        pass
+
+
+n, m, data = parse_file("data/GKD-c_11_n500_m50.txt")
+genetic_algorithm(n=10, m=5, data=data, mode="")