Python algorithms.eaSimple方法代码示例

# 需要导入模块: from deap import algorithms [as 别名]
# 或者: from deap.algorithms import eaSimple [as 别名]
def main():
    random.seed(169)

    pop = toolbox.population(n=300)

    hof = tools.HallOfFame(1)
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("avg", numpy.mean)
    stats.register("std", numpy.std)
    stats.register("min", numpy.min)
    stats.register("max", numpy.max)
    
    algorithms.eaSimple(pop, toolbox, 0.7, 0.2, 40, stats=stats, 
                        halloffame=hof)
    
    return pop, stats, hof 

# 需要导入模块: from deap import algorithms [as 别名]
# 或者: from deap.algorithms import eaSimple [as 别名]
def main():
    random.seed(64)
    
    pop = toolbox.population(n=300)
    
    # Numpy equality function (operators.eq) between two arrays returns the
    # equality element wise, which raises an exception in the if similar()
    # check of the hall of fame. Using a different equality function like
    # numpy.array_equal or numpy.allclose solve this issue.
    hof = tools.HallOfFame(1, similar=numpy.array_equal)
    
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("avg", numpy.mean)
    stats.register("std", numpy.std)
    stats.register("min", numpy.min)
    stats.register("max", numpy.max)
    
    algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=40, stats=stats,
                        halloffame=hof)

    return pop, stats, hof 

# 需要导入模块: from deap import algorithms [as 别名]
# 或者: from deap.algorithms import eaSimple [as 别名]
def main():
    random.seed(64)
    NISLES = 5
    islands = [toolbox.population(n=300) for i in range(NISLES)]

    # Unregister unpicklable methods before sending the toolbox.
    toolbox.unregister("attr_bool")
    toolbox.unregister("individual")
    toolbox.unregister("population")

    NGEN, FREQ = 40, 5
    toolbox.register("algorithm", algorithms.eaSimple, toolbox=toolbox, 
                     cxpb=0.5, mutpb=0.2, ngen=FREQ, verbose=False)
    for i in range(0, NGEN, FREQ):
        results = toolbox.map(toolbox.algorithm, islands)
        islands = [pop for pop, logbook in results]
        tools.migRing(islands, 15, tools.selBest)

    return islands 

# 需要导入模块: from deap import algorithms [as 别名]
# 或者: from deap.algorithms import eaSimple [as 别名]
def main():
    random.seed(69)
    
    with  open("ant/santafe_trail.txt") as trail_file:
      ant.parse_matrix(trail_file)
    
    pop = toolbox.population(n=300)
    hof = tools.HallOfFame(1)
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("avg", numpy.mean)
    stats.register("std", numpy.std)
    stats.register("min", numpy.min)
    stats.register("max", numpy.max)
    
    algorithms.eaSimple(pop, toolbox, 0.5, 0.2, 40, stats, halloffame=hof)
    
    return pop, hof, stats 

# 需要导入模块: from deap import algorithms [as 别名]
# 或者: from deap.algorithms import eaSimple [as 别名]
def main():
    #random.seed(318)

    pop = toolbox.population(n=300)
    hof = tools.HallOfFame(1)
    
    stats_fit = tools.Statistics(lambda ind: ind.fitness.values)
    stats_size = tools.Statistics(len)
    mstats = tools.MultiStatistics(fitness=stats_fit, size=stats_size)
    mstats.register("avg", numpy.mean)
    mstats.register("std", numpy.std)
    mstats.register("min", numpy.min)
    mstats.register("max", numpy.max)

    pop, log = algorithms.eaSimple(pop, toolbox, 0.5, 0.1, 40, stats=mstats,
                                   halloffame=hof, verbose=True)
    # print log
    return pop, log, hof 

# 需要导入模块: from deap import algorithms [as 别名]
# 或者: from deap.algorithms import eaSimple [as 别名]
def main():

    # create initial population (generation 0):
    population = toolbox.populationCreator(n=POPULATION_SIZE)

    # prepare the statistics object:
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("max", numpy.max)
    stats.register("avg", numpy.mean)

    # define the hall-of-fame object:
    hof = tools.HallOfFame(HALL_OF_FAME_SIZE)

    # perform the Genetic Algorithm flow with hof feature added:
    population, logbook = algorithms.eaSimple(population, toolbox, cxpb=P_CROSSOVER, mutpb=P_MUTATION,
                                              ngen=MAX_GENERATIONS, stats=stats, halloffame=hof, verbose=True)

    # print best solution found:
    best = hof.items[0]
    print("-- Best Ever Individual = ", best)
    print("-- Best Ever Fitness = ", best.fitness.values[0])

    print("-- Knapsack Items = ")
    knapsack.printItems(best)

    # extract statistics:
    maxFitnessValues, meanFitnessValues = logbook.select("max", "avg")

    # plot statistics:
    sns.set_style("whitegrid")
    plt.plot(maxFitnessValues, color='red')
    plt.plot(meanFitnessValues, color='green')
    plt.xlabel('Generation')
    plt.ylabel('Max / Average Fitness')
    plt.title('Max and Average fitness over Generations')
    plt.show() 

# 需要导入模块: from deap import algorithms [as 别名]
# 或者: from deap.algorithms import eaSimple [as 别名]
def main():
    random.seed(64)
    
    pop = toolbox.population(n=300)
    hof = tools.HallOfFame(1)
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("avg", numpy.mean)
    stats.register("std", numpy.std)
    stats.register("min", numpy.min)
    stats.register("max", numpy.max)
    
    pop, log = algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=40, 
                                   stats=stats, halloffame=hof, verbose=True)
    
    return pop, log, hof 

# 需要导入模块: from deap import algorithms [as 别名]
# 或者: from deap.algorithms import eaSimple [as 别名]
def geneticAlgorithm(X, y, n_population, n_generation):
    """
    Deap global variables
    Initialize variables to use eaSimple
    """
    # create individual
    creator.create("FitnessMax", base.Fitness, weights=(1.0,))
    creator.create("Individual", list, fitness=creator.FitnessMax)

    # create toolbox
    toolbox = base.Toolbox()
    toolbox.register("attr_bool", random.randint, 0, 1)
    toolbox.register("individual", tools.initRepeat,
                     creator.Individual, toolbox.attr_bool, len(X.columns))
    toolbox.register("population", tools.initRepeat, list,
                     toolbox.individual)
    toolbox.register("evaluate", getFitness, X=X, y=y)
    toolbox.register("mate", tools.cxOnePoint)
    toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
    toolbox.register("select", tools.selTournament, tournsize=3)

    # initialize parameters
    pop = toolbox.population(n=n_population)
    hof = tools.HallOfFame(n_population * n_generation)
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("avg", np.mean)
    stats.register("min", np.min)
    stats.register("max", np.max)

    # genetic algorithm
    pop, log = algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2,
                                   ngen=n_generation, stats=stats, halloffame=hof,
                                   verbose=True)

    # return hall of fame
    return hof 

# 需要导入模块: from deap import algorithms [as 别名]
# 或者: from deap.algorithms import eaSimple [as 别名]
def main():

    # create initial population (generation 0):
    population = toolbox.populationCreator(n=POPULATION_SIZE)

    # prepare the statistics object:
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("min", np.min)
    stats.register("avg", np.mean)

    # define the hall-of-fame object:
    hof = tools.HallOfFame(HALL_OF_FAME_SIZE)

    # perform the Genetic Algorithm flow with hof feature added:
    population, logbook = algorithms.eaSimple(population, toolbox, cxpb=P_CROSSOVER, mutpb=P_MUTATION,
                                              ngen=MAX_GENERATIONS, stats=stats, halloffame=hof, verbose=True)

    # print best individual info:
    best = hof.items[0]
    print("-- Best Ever Individual = ", best)
    print("-- Best Ever Fitness = ", best.fitness.values[0])

    # plot best solution:
    plt.figure(1)
    tsp.plotData(best)

    # plot statistics:
    minFitnessValues, meanFitnessValues = logbook.select("min", "avg")
    plt.figure(2)
    sns.set_style("whitegrid")
    plt.plot(minFitnessValues, color='red')
    plt.plot(meanFitnessValues, color='green')
    plt.xlabel('Generation')
    plt.ylabel('Min / Average Fitness')
    plt.title('Min and Average fitness over Generations')

    # show both plots:
    plt.show() 

# 需要导入模块: from deap import algorithms [as 别名]
# 或者: from deap.algorithms import eaSimple [as 别名]
def main():

    # create initial population (generation 0):
    population = toolbox.populationCreator(n=POPULATION_SIZE)

    # prepare the statistics object:
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("max", numpy.max)
    stats.register("avg", numpy.mean)

    # perform the Genetic Algorithm flow:
    population, logbook = algorithms.eaSimple(population, toolbox, cxpb=P_CROSSOVER, mutpb=P_MUTATION, ngen=MAX_GENERATIONS,
                                   stats=stats, verbose=True)


    # Genetic Algorithm is done - extract statistics:
    maxFitnessValues, meanFitnessValues = logbook.select("max", "avg")

    # plot statistics:
    sns.set_style("whitegrid")
    plt.plot(maxFitnessValues, color='red')
    plt.plot(meanFitnessValues, color='green')
    plt.xlabel('Generation')
    plt.ylabel('Max / Average Fitness')
    plt.title('Max and Average Fitness over Generations')
    plt.show() 

# 需要导入模块: from deap import algorithms [as 别名]
# 或者: from deap.algorithms import eaSimple [as 别名]
def main():

    # create initial population (generation 0):
    population = toolbox.populationCreator(n=POPULATION_SIZE)

    # prepare the statistics object:
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("max", numpy.max)
    stats.register("avg", numpy.mean)

    # define the hall-of-fame object:
    hof = tools.HallOfFame(HALL_OF_FAME_SIZE)

    # perform the Genetic Algorithm flow with hof feature added:
    population, logbook = algorithms.eaSimple(population, toolbox, cxpb=P_CROSSOVER, mutpb=P_MUTATION,
                                              ngen=MAX_GENERATIONS, stats=stats, halloffame=hof, verbose=True)

    # print Hall of Fame info:
    print("Hall of Fame Individuals = ", *hof.items, sep="\n")
    print("Best Ever Individual = ", hof.items[0])

    # extract statistics:
    maxFitnessValues, meanFitnessValues = logbook.select("max", "avg")

    # plot statistics:
    sns.set_style("whitegrid")
    plt.plot(maxFitnessValues, color='red')
    plt.plot(meanFitnessValues, color='green')
    plt.xlabel('Generation')
    plt.ylabel('Max / Average Fitness')
    plt.title('Max and Average Fitness over Generations')

    plt.show() 

# 需要导入模块: from deap import algorithms [as 别名]
# 或者: from deap.algorithms import eaSimple [as 别名]
def run(num_gen,
        n,
        mutpb,
        cxpb):
    """
    Runs multiple episodes, evolving the RNN parameters using a GA
    """
    history = tools.History()
    # Decorate the variation operators
    toolbox.decorate("mate", history.decorator)
    toolbox.decorate("mutate", history.decorator)

    pool = multiprocessing.Pool(processes=12)
    toolbox.register("map", pool.map)

    pop = toolbox.population(n=n)
    history.update(pop)

    hof = tools.HallOfFame(1)
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("avg", np.mean)
    stats.register("std", np.std)
    stats.register("min", np.min)
    stats.register("max", np.max)

    pop, log = algorithms.eaSimple(pop,
                                   toolbox,
                                   cxpb=cxpb,
                                   mutpb=mutpb,
                                   ngen=num_gen,
                                   stats=stats,
                                   halloffame=hof,
                                   verbose=True)

    return pop, log, hof, history


# Set up the genetic algorithm to evolve the RNN parameters 

# 需要导入模块: from deap import algorithms [as 别名]
# 或者: from deap.algorithms import eaSimple [as 别名]
def main(seed=0):
    random.seed(seed)

    pop = toolbox.population(n=300)
    hof = tools.HallOfFame(1)
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("Avg", numpy.mean)
    stats.register("Std", numpy.std)
    stats.register("Min", numpy.min)
    stats.register("Max", numpy.max)

    algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=100, stats=stats,
                        halloffame=hof, verbose=True)

    return pop, stats, hof 

# 需要导入模块: from deap import algorithms [as 别名]
# 或者: from deap.algorithms import eaSimple [as 别名]
def main():
    random.seed(318)

    pop = toolbox.population(n=300)
    hof = tools.HallOfFame(1)
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("avg", numpy.mean)
    stats.register("std", numpy.std)
    stats.register("min", numpy.min)
    stats.register("max", numpy.max)
    
    algorithms.eaSimple(pop, toolbox, 0.5, 0.1, 40, stats, halloffame=hof)

    return pop, stats, hof 

# 需要导入模块: from deap import algorithms [as 别名]
# 或者: from deap.algorithms import eaSimple [as 别名]
def main():
#    random.seed(10)
    pop = toolbox.population(n=40)
    hof = tools.HallOfFame(1)
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("avg", numpy.mean)
    stats.register("std", numpy.std)
    stats.register("min", numpy.min)
    stats.register("max", numpy.max)
    
    algorithms.eaSimple(pop, toolbox, 0.8, 0.1, 40, stats, halloffame=hof)
    
    return pop, stats, hof