Implement the stationary replacement operator

src/genetic_algorithm.py

@@ -158,15 +158,34 @@ def generational_replacement(previous_population, current_population):
     return new_population
 
 
-def stationary_replacement(previous_population, current_population):
-    new_population = previous_population
+def get_best_elements(population):
+    first_index = population.index(max(population, key=lambda x: x.fitness))
+    population.pop(first_index)
+    second_index = population.index(max(population, key=lambda x: x.fitness))
+    return first_index, second_index
+
+
+def get_worst_elements(population):
+    first_index = population.index(min(population, key=lambda x: x.fitness))
+    population.pop(first_index)
+    second_index = population.index(min(population, key=lambda x: x.fitness))
+    return first_index, second_index
+
+
+def stationary_replacement(prev_population, current_population):
+    new_population = prev_population
+    worst_indexes = get_worst_elements(prev_population)
+    best_indexes = get_best_elements(current_population)
+    for worst, best in zip(worst_indexes, best_indexes):
+        if current_population[best].fitness > prev_population[worst].fitness:
+            new_population[worst] = current_population[best]
     return new_population
 
 
-def replace_population(previous_population, current_population, mode):
+def replace_population(prev_population, current_population, mode):
     if mode == "generational":
-        return generational_replacement(previous_population, current_population)
-    return stationary_replacement(previous_population, current_population)
+        return generational_replacement(prev_population, current_population)
+    return stationary_replacement(prev_population, current_population)
 
 
 def genetic_algorithm(n, m, data, mode):