Implement the generational replacement operator

src/genetic_algorithm.py

@@ -149,5 +149,25 @@ def tournament_selection(population):
     return individuals.iloc[best_index]
 
 
-def genetic_algorithm(n, m, data):
+def generational_replacement(previous_population, current_population):
-    first_solution = generate_first_solution(n, m, data)
+    new_population = current_population
+    best_previous_individual = max(previous_population, key=lambda x: x.fitness)
+    if best_previous_individual not in new_population:
+        worst_index = new_population.index(min(new_population, key=lambda x: x.fitness))
+        new_population[worst_index] = best_previous_individual
+    return new_population
+
+
+def stationary_replacement(previous_population, current_population):
+    new_population = previous_population
+    return new_population
+
+
+def replace_population(previous_population, current_population, mode):
+    if mode == "generational":
+        return generational_replacement(previous_population, current_population)
+    return stationary_replacement(previous_population, current_population)
+
+
+def genetic_algorithm(n, m, data, mode):
+    population = [generate_individual(n, m, data) for _ in range(n)]