diff --git a/src/main.py b/src/main.py
index a56e594..035d552 100755
--- a/src/main.py
+++ b/src/main.py
@@ -4,16 +4,16 @@ from memetic_algorithm import memetic_algorithm
 from sys import argv
 from time import time
 from itertools import combinations
+from argparse import ArgumentParser
 
 
-def execute_algorithm(choice, n, m, data):
-    if choice == "genetic":
-        return genetic_algorithm(n, m, data)
-    elif choice == "memetic":
-        return memetic_algorithm(m, data)
+def execute_algorithm(args, n, m, data):
+    if args.algorithm == "genetic":
+        return genetic_algorithm(
+            n, m, data, select_mode=args.selection, crossover_mode=args.crossover
+        )
     else:
-        print("The valid algorithm choices are 'genetic' and 'memetic'")
-        exit(1)
+        return memetic_algorithm(n, m, data, hybridation=args.hybridation)
 
 
 def get_row_distance(source, destination, data):
@@ -47,19 +47,30 @@ def show_results(solutions, fitness, time_delta):
 
 
 def usage(argv):
-    print(f"Usage: python {argv[0]} <file> <algorithm choice>")
+    print(f"Usage: python {argv[0]} <file> <algorithm choice> <")
     print("algorithm choices:")
     print("genetic: genetic algorithm")
     print("memetic: memetic algorithm")
     exit(1)
 
 
+def parse_arguments():
+    parser = ArgumentParser()
+    parser.add_argument("file", help="dataset of choice")
+    subparsers = parser.add_subparsers(dest="algorithm")
+    parser_genetic = subparsers.add_parser("genetic")
+    parser_memetic = subparsers.add_parser("memetic")
+    parser_genetic.add_argument("crossover", choices=["uniform", "position"])
+    parser_genetic.add_argument("selection", choices=["generational", "stationary"])
+    parser_memetic.add_argument("hybridation", choices=["all", "random", "best"])
+    return parser.parse_args()
+
+
 def main():
-    if len(argv) != 3:
-        usage(argv)
-    n, m, data = parse_file(argv[1])
+    args = parse_arguments()
+    n, m, data = parse_file(args.file)
     start_time = time()
-    solutions = execute_algorithm(choice=argv[2], n=n, m=m, data=data)
+    solutions = execute_algorithm(args, n, m, data)
     end_time = time()
     fitness = get_fitness(solutions, data)
     show_results(solutions, fitness, time_delta=end_time - start_time)