diff --git a/src/genetic_algorithm.py b/src/genetic_algorithm.py
index 3567ba6..9357842 100644
--- a/src/genetic_algorithm.py
+++ b/src/genetic_algorithm.py
@@ -1,10 +1,14 @@
 from numpy.random import choice, seed
 
 
-def get_first_random_solution(m, data):
+def get_first_random_solution(n, m):
     seed(42)
-    random_indexes = choice(len(data.index), size=m, replace=False)
-    return data.loc[random_indexes]
+    solution = zeros(shape=n, dtype=bool)
+    random_indices = choice(n, size=m, replace=False)
+    put(solution, ind=random_indices, v=True)
+    return solution
+
+
 
 
 def element_in_dataframe(solution, element):
@@ -42,8 +46,8 @@ def explore_neighbourhood(element, data, max_iterations=100000):
     return neighbour
 
 
-def local_search(m, data):
-    first_solution = get_first_random_solution(m=m, data=data)
+def genetic_algorithm(n, m, data):
+    first_solution = get_first_random_solution(n=n, m=m)
     best_solution = explore_neighbourhood(
         element=first_solution, data=data, max_iterations=100
     )
diff --git a/src/main.py b/src/main.py
index f70aaa1..9286f13 100755
--- a/src/main.py
+++ b/src/main.py
@@ -1,17 +1,17 @@
 from preprocessing import parse_file
-from greedy import greedy_algorithm
-from local_search import local_search
+from genetic_algorithm import genetic_algorithm
+from memetic_algorithm import memetic_algorithm
 from sys import argv
 from time import time
 
 
 def execute_algorithm(choice, n, m, data):
-    if choice == "greedy":
-        return greedy_algorithm(n, m, data)
-    elif choice == "local":
-        return local_search(m, data)
+    if choice == "genetic":
+        return genetic_algorithm(n, m, data)
+    elif choice == "memetic":
+        return memetic_algorithm(m, data)
     else:
-        print("The valid algorithm choices are 'greedy' and 'local'")
+        print("The valid algorithm choices are 'genetic' and 'memetic'")
         exit(1)
 
 
@@ -28,8 +28,8 @@ def show_results(solutions, time_delta):
 def usage(argv):
     print(f"Usage: python {argv[0]} <file> <algorithm choice>")
     print("algorithm choices:")
-    print("greedy: greedy algorithm")
-    print("local: local search algorithm")
+    print("genetic: genetic algorithm")
+    print("memetic: memetic algorithm")
     exit(1)
 
 
diff --git a/src/memetic_algorithm.py b/src/memetic_algorithm.py
new file mode 100644
index 0000000..1a6425f
--- /dev/null
+++ b/src/memetic_algorithm.py
@@ -0,0 +1,50 @@
+from numpy.random import choice, seed
+
+
+def get_first_random_solution(m, data):
+    seed(42)
+    random_indexes = choice(len(data.index), size=m, replace=False)
+    return data.loc[random_indexes]
+
+
+def element_in_dataframe(solution, element):
+    duplicates = solution.query(
+        f"(source == {element.source} and destination == {element.destination}) or (source == {element.destination} and destination == {element.source})"
+    )
+    return not duplicates.empty
+
+
+def replace_worst_element(previous, data):
+    solution = previous.copy()
+    worst_index = solution["distance"].astype(float).idxmin()
+    random_element = data.sample().squeeze()
+    while element_in_dataframe(solution=solution, element=random_element):
+        random_element = data.sample().squeeze()
+    solution.loc[worst_index] = random_element
+    return solution, worst_index
+
+
+def get_random_solution(previous, data):
+    solution, worst_index = replace_worst_element(previous, data)
+    previous_worst_distance = previous["distance"].loc[worst_index]
+    while solution.distance.loc[worst_index] <= previous_worst_distance:
+        solution, _ = replace_worst_element(previous=solution, data=data)
+    return solution
+
+
+def explore_neighbourhood(element, data, max_iterations=100000):
+    neighbourhood = []
+    neighbourhood.append(element)
+    for _ in range(max_iterations):
+        previous_solution = neighbourhood[-1]
+        neighbour = get_random_solution(previous=previous_solution, data=data)
+        neighbourhood.append(neighbour)
+    return neighbour
+
+
+def memetic_algorithm(m, data):
+    first_solution = get_first_random_solution(m=m, data=data)
+    best_solution = explore_neighbourhood(
+        element=first_solution, data=data, max_iterations=100
+    )
+    return best_solution