Implement uniform generational genetic algorithm

src/genetic_algorithm.py

@@ -4,6 +4,7 @@ from pandas import DataFrame
 from math import ceil
 from functools import partial
 from multiprocessing import Pool
+from copy import deepcopy
 
 from preprocessing import parse_file
 
@@ -169,7 +170,7 @@ def mutate(offspring, data, probability=0.001):
     return offspring
 
 
-def get_individual_index(population, element):
+def get_individual_index(element, population):
     for index in range(len(population)):
         if population[index].fitness.values[0] == element.fitness.values[0]:
             return index
@@ -178,25 +179,34 @@ def get_individual_index(population, element):
 def tournament_selection(population):
     individuals = [population[randint(len(population))] for _ in range(2)]
     best_element = max(individuals, key=lambda x: x.fitness.values[0])
-    population_index = get_individual_index(population, best_element)
+    population_index = get_individual_index(best_element, population)
     return best_element, population_index
 
 
-def generational_replacement(previous_population, current_population):
+def check_element_population(element, population):
+    for item in population:
+        if all(element.point.values) == all(item.point.values):
+            return True
+    return False
+
+
+def generational_replacement(prev_population, current_population):
     new_population = current_population
-    best_previous_individual = max(previous_population, key=lambda x: all(x.fitness))
-    if best_previous_individual not in new_population:
-        worst_index = new_population.index(
-            min(new_population, key=lambda x: all(x.fitness))
-        )
+    best_previous_individual = max(prev_population, key=lambda x: x.fitness.values[0])
+    if check_element_population(best_previous_individual, new_population):
+        worst_element = min(new_population, key=lambda x: x.fitness.values[0])
+        worst_index = get_individual_index(worst_element, new_population)
         new_population[worst_index] = best_previous_individual
     return new_population
 
 
 def get_best_elements(population):
-    first_index = population.index(max(population, key=lambda x: all(x.fitness)))
-    population.pop(first_index)
-    second_index = population.index(max(population, key=lambda x: all(x.fitness)))
+    select_population = deepcopy(population)
+    first_element = max(select_population, key=lambda x: x.fitness.values[0])
+    first_index = get_individual_index(first_element, select_population)
+    select_population.pop(first_index)
+    second_element = max(select_population, key=lambda x: x.fitness.values[0])
+    second_index = get_individual_index(second_element, select_population)
     return first_index, second_index
 
 
@@ -231,7 +241,7 @@ def evaluate_population(population, data, cores=4):
 
 
 def select_parents(population, n, mode):
-    select_population = population
+    select_population = deepcopy(population)
     parents = []
     if mode == "generational":
         for _ in range(n):
@@ -255,8 +265,8 @@ def genetic_algorithm(n, m, data, select_mode, crossover_mode, max_iterations=10
         offspring = mutate(offspring, data)
         population = replace_population(population, offspring, select_mode)
         population = evaluate_population(population, data)
-    best_solution, _ = get_best_elements(population)
-    return best_solution
+    best_index, _ = get_best_elements(population)
+    return population[best_index]
 
 
 n, m, data = parse_file("data/GKD-c_11_n500_m50.txt")