Add memetic algorithm prototype

src/memetic_algorithm.py

@@ -1,50 +1,22 @@
-from numpy.random import choice, seed
+from genetic_algorithm import *
+from local_search import local_search
 
 
-def get_first_random_solution(m, data):
-    seed(42)
-    random_indexes = choice(len(data.index), size=m, replace=False)
-    return data.loc[random_indexes]
+def run_local_search(n, m, data, individual):
+    pass
 
 
-def element_in_dataframe(solution, element):
-    duplicates = solution.query(
-        f"(source == {element.source} and destination == {element.destination}) or (source == {element.destination} and destination == {element.source})"
-    )
-    return not duplicates.empty
-
-
-def replace_worst_element(previous, data):
-    solution = previous.copy()
-    worst_index = solution["distance"].astype(float).idxmin()
-    random_element = data.sample().squeeze()
-    while element_in_dataframe(solution=solution, element=random_element):
-        random_element = data.sample().squeeze()
-    solution.loc[worst_index] = random_element
-    return solution, worst_index
-
-
-def get_random_solution(previous, data):
-    solution, worst_index = replace_worst_element(previous, data)
-    previous_worst_distance = previous["distance"].loc[worst_index]
-    while solution.distance.loc[worst_index] <= previous_worst_distance:
-        solution, _ = replace_worst_element(previous=solution, data=data)
-    return solution
-
-
-def explore_neighbourhood(element, data, max_iterations=100000):
-    neighbourhood = []
-    neighbourhood.append(element)
-    for _ in range(max_iterations):
-        previous_solution = neighbourhood[-1]
-        neighbour = get_random_solution(previous=previous_solution, data=data)
-        neighbourhood.append(neighbour)
-    return neighbour
-
-
-def memetic_algorithm(m, data):
-    first_solution = get_first_random_solution(m=m, data=data)
-    best_solution = explore_neighbourhood(
-        element=first_solution, data=data, max_iterations=100
-    )
-    return best_solution
+def memetic_algorithm(n, m, data, hybridation, max_iterations=100000):
+    population = [generate_individual(n, m, data) for _ in range(n)]
+    population = evaluate_population(population, data)
+    for i in range(max_iterations):
+        if i % 10 == 0:
+            best_index, _ = get_best_elements(population)
+            run_local_search(n, m, data, individual=population[best_index])
+        parents = select_parents(population, n, mode="stationary")
+        offspring = crossover(mode="uniform", parents=parents, m=m)
+        offspring = mutate(offspring, n, data)
+        population = replace_population(population, offspring, mode="stationary")
+        population = evaluate_population(population, data)
+    best_index, _ = get_best_elements(population)
+    return population[best_index]