I've been coding a Genetic Algorithm to solve TSP using python's DEAP library. I've implemented my own Graph class, and an Algorithm class that runs the GA on a Graph instance. However, when adding paralelization inside the Algorithms._define_toolbox() method, my main class takes longer. I've been reading how to apply serialization using the multiproccesing library with DEAP, and I know that having a main method is required, but my GA is called from main and it runs slower than not using paralellization. Here is the relevant code: algorithms.py class Algorithms(): # prev. code def _define_creator(self) -> creator: """Defines a deap creator for the genetic algorithms. The ``deap.creator`` module is part of the DEAP framework and it's used to extend existing classes, adding new functionalities to them. This function extracts the ``creator`` instantiation from the ``run_ga_tsp`` function so the code is easier to read and follow. Inside the ``creator`` object is where the objective of the genetic algorithm is defined, as well as what will the individuals be like. In this case, the objective is to minimize the value of the objective function, and the individuals are lists of integers, containing the indices of the nodes of the graph in the order they will be visited. Returns: The creator defined for the genetic algorithm. """ if hasattr(creator, 'FitnessMin'): del creator.FitnessMin if hasattr(creator, 'Individual'): del creator.Individual creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", list, typecode='i', fitness=creator.FitnessMin) return creator def _define_toolbox(self) -> base.Toolbox: """Defines a deap toolbox for the genetic algorithms. The ``deap.base.createor`` module is part of the DEAP framework. It's used as a container for functions, and enables the creation of new operators by customizing existing ones. This function extracts the ``toolbox`` instantiation from the ``run_ga_tsp`` function so the code is easier to read and follow. In the ``toolbox`` object is where the functions used by the genetic algorithm are defined, such as the evaluation, selection, crossover and mutation functions. Returns: The toolbox defined for the genetic algorithm. """ nodes = [node.index for node in self.graph.node_list] genes = [i for i in range(len(nodes))] self.convert = {i: node for i, node in enumerate(nodes)} toolbox = base.Toolbox() toolbox.register("random_order", random.sample, genes, len(nodes)) toolbox.register("individual_creator", tools.initIterate, creator.Individual, toolbox.random_order) toolbox.register("population_creator", tools.initRepeat, list, toolbox.individual_creator) toolbox.register("evaluate", self.evaluate_tsp) toolbox.register("select", tools.selTournament, tournsize=2) toolbox.register("mate", tools.cxOrdered) toolbox.register("mutate", tools.mutShuffleIndexes, indpb=1.0 / self.graph.nodes) return toolbox def _define_ga(self, toolbox: base.Toolbox, pop_size: int) -> tuple[list, dict, list]: """Defines the attributes for the Generic Algorithm. The function defines the population, statistics and hall of fame for the Genetic Algorithm designed to solve the Traveling Salesman Problem. Args: toolbox: The toolbox for the genetic algorithm. pop_size: The size of the population. Returns: A tuple containing the population, statistics and hall of fame. """ population = toolbox.population_creator(n=pop_size) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("min", np.min) stats.register("avg", np.mean) hof = tools.HallOfFame(30) return population, stats, hof def run_ga_tsp(self, ngen: int = 100, cxpb: float = 0.9, mutpb: float = 0.1, pop_size: int = 200, dir: str | None = None, idx: int = 0, vrb: bool = True) -> tuple[list[int], float]: """Runs the Genetic Algorithm for the Traveling Salesman Problem. This function calls the wrapper functions that define the creator, toolbox and the attributes for the Genetic Algorithm designed to solve the Traveling Salesman Problem. It then runs the Genetic Algorithm and returns the best path found and its total value, while also calling the wrapper function to plot the results. Args: ngen (optional): The number of generations. Defaults to 100. cxpb (optional): The mating probability. Defaults to 0.9. mutpb (optional): The mutation probability. Defaults to 0.1. pop_size (optional): The size of the population. Defaults to 200. dir (optional): The directory where the plots should be saved. Defaults to None, in which case the plot(s) won't be saved. idx (optional): The index for the plot to save. Defaults to 0. vrb: (optional): Run the algorithm in verbose or non-verbose mode. Defaults to True. Returns: A tuple containing the best path found and its total value. """ creator = self._define_creator() toolbox = self._define_toolbox() population, stats, hof, = self._define_ga(toolbox, pop_size) population, logbook = self.eaSimpleWithElitism(population, toolbox, cxpb=cxpb, mutpb=mutpb, ngen=ngen, stats=stats, halloffame=hof, verbose=vrb) best = [self.convert for i in hof.items[0]] best_path = ([self.graph.center.index] + best + [self.graph.center.index]) total_value = self.evaluate_tsp(hof[0])[0] if vrb: print("-- Best Ever Individual = ", best_path) print("-- Best Ever Fitness = ", hof.items[0].fitness.values[0]) if dir: self._plot_ga_results(best_path, logbook, dir, idx) else: self._plot_ga_results(best_path, logbook).show() return best_path, total_value main.py import os from model import Graph from algorithms import Algorithms def run(): """An example of using the model & algorithms modules.""" g = Graph() print("Loading graph") g.populate_from_file(os.getcwd() + "/files/test2.txt") #g.populate_from_file(os.getcwd() + "/Algorithm/AlgoCode/files/test2.txt") print("Graph loaded") algo = Algorithms(g) _, v = algo.run_ga_tsp(ngen=500, pop_size=500, idx=0, dir=os.getcwd() + "/plots", vrb=False) print(f"Total value (TSP): {v}") def main(): run() if __name__ == '__main__': main() As stated, I've tried adding pool = multiprocessing.Pool() toolbox.register("map", pool.map) to Algorithms._define_toolbox(), but to no avail. 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