def METHOD_NAME(seed=None):
    random.seed(seed)
    # Initialize statistics object
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("avg", numpy.mean, axis=0)
    stats.register("std", numpy.std, axis=0)
    stats.register("min", numpy.min, axis=0)
    stats.register("max", numpy.max, axis=0)
    logbook = tools.Logbook()
    logbook.header = "gen", "evals", "std", "min", "avg", "max"
    pop = toolbox.population(n=MU)
    # Evaluate the individuals with an invalid fitness
    invalid_ind = [ind for ind in pop if not ind.fitness.valid]
    fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
    for ind, fit in zip(invalid_ind, fitnesses):
        ind.fitness.values = fit
    # Compile statistics about the population
    record = stats.compile(pop)
    logbook.record(gen=0, evals=len(invalid_ind), **record)
    print(logbook.stream)
    # Begin the generational process
    for gen in range(1, NGEN):
        offspring = algorithms.varAnd(pop, toolbox, CXPB, MUTPB)
        # Evaluate the individuals with an invalid fitness
        invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
        fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
        for ind, fit in zip(invalid_ind, fitnesses):
            ind.fitness.values = fit
        # Select the next generation population from parents and offspring
        pop = toolbox.select(pop + offspring, MU)
        # Compile statistics about the new population
        record = stats.compile(pop)
        logbook.record(gen=gen, evals=len(invalid_ind), **record)
        print(logbook.stream)
    return pop, logbook