src/genetic_algorithm.py

@@ -6,6 +6,8 @@ from functools import partial
 from multiprocessing import Pool
 from copy import deepcopy
 
+from preprocessing import parse_file
+
 
 def get_row_distance(source, destination, data):
     row = data.query(
@@ -176,7 +178,7 @@ def select_new_gene(individual, n):
             return new_gene
 
 
-def mutate(offspring, n, data, probability=0.001):
+def mutate(offspring, data, probability=0.001):
     expected_mutations = len(offspring) * n * probability
     individuals = []
     genes = []
@@ -295,8 +297,19 @@ def genetic_algorithm(n, m, data, select_mode, crossover_mode, max_iterations=10
     for _ in range(max_iterations):
         parents = select_parents(population, n, select_mode)
         offspring = crossover(crossover_mode, parents, m)
-        offspring = mutate(offspring, n, data)
+        offspring = mutate(offspring, data)
         population = replace_population(population, offspring, select_mode)
         population = evaluate_population(population, data)
     best_index, _ = get_best_elements(population)
     return population[best_index]
+
+
+n, m, data = parse_file("data/GKD-c_11_n500_m50.txt")
+genetic_algorithm(
+    n=10,
+    m=4,
+    data=data,
+    select_mode="generational",
+    crossover_mode="uniform",
+    max_iterations=10,
+)

src/main.py

@@ -4,27 +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(args, n, m, data):
-    if args.algorithm == "genetic":
-        return genetic_algorithm(
-            n,
-            m,
-            data,
-            select_mode=args.selection,
-            crossover_mode=args.crossover,
-            max_iterations=100,
-        )
+def execute_algorithm(choice, n, m, data):
+    if choice == "genetic":
+        return genetic_algorithm(n, m, data)
+    elif choice == "memetic":
+        return memetic_algorithm(m, data)
     else:
-        return memetic_algorithm(
-            n,
-            m,
-            data,
-            hybridation=args.hybridation,
-            max_iterations=100,
-        )
+        print("The valid algorithm choices are 'genetic' and 'memetic'")
+        exit(1)
 
 
 def get_row_distance(source, destination, data):
@@ -58,30 +47,19 @@ 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():
-    args = parse_arguments()
-    n, m, data = parse_file(args.file)
+    if len(argv) != 3:
+        usage(argv)
+    n, m, data = parse_file(argv[1])
     start_time = time()
-    solutions = execute_algorithm(args, n, m, data)
+    solutions = execute_algorithm(choice=argv[2], n=n, m=m, data=data)
     end_time = time()
     fitness = get_fitness(solutions, data)
     show_results(solutions, fitness, time_delta=end_time - start_time)