Python base.Fitness方法代码示例

# 需要导入模块: from deap import base [as 别名]
# 或者: from deap.base import Fitness [as 别名]
def _setup_toolbox(self):
        with warnings.catch_warnings():
            warnings.simplefilter('ignore')
            creator.create('FitnessMulti', base.Fitness, weights=(-1.0, 1.0))
            creator.create('Individual', gp.PrimitiveTree, fitness=creator.FitnessMulti, statistics=dict)

        self._toolbox = base.Toolbox()
        self._toolbox.register('expr', self._gen_grow_safe, pset=self._pset, min_=self._min, max_=self._max)
        self._toolbox.register('individual', tools.initIterate, creator.Individual, self._toolbox.expr)
        self._toolbox.register('population', tools.initRepeat, list, self._toolbox.individual)
        self._toolbox.register('compile', self._compile_to_sklearn)
        self._toolbox.register('select', tools.selNSGA2)
        self._toolbox.register('mate', self._mate_operator)
        if self.tree_structure:
            self._toolbox.register('expr_mut', self._gen_grow_safe, min_=self._min, max_=self._max + 1)
        else:
            self._toolbox.register('expr_mut', self._gen_grow_safe, min_=self._min, max_=self._max)
        self._toolbox.register('mutate', self._random_mutation_operator) 

# 需要导入模块: from deap import base [as 别名]
# 或者: from deap.base import Fitness [as 别名]
def getGlobalToolbox(representationModule):
    # GLOBAL FUNCTION TO GET GLOBAL TBX UNDER DEVELOPMENT;
    toolbox = base.Toolbox()
    creator.create("FitnessMax", base.Fitness, weights=(1.0,))
    creator.create(
        "Individual",
        list,
        fitness=creator.FitnessMax,
        PromoterMap=None,
        Strategy=genconf.Strategy,
    )
    toolbox.register("mate", representationModule.crossover)
    toolbox.register("mutate", representationModule.mutate)
    PromoterMap = initPromoterMap(Attributes)
    toolbox.register("newind", initInd, creator.Individual, PromoterMap)
    toolbox.register("population", tools.initRepeat, list, toolbox.newind)
    toolbox.register("constructPhenotype", representationModule.constructPhenotype)
    return toolbox 

# 需要导入模块: from deap import base [as 别名]
# 或者: from deap.base import Fitness [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 base [as 别名]
# 或者: from deap.base import Fitness [as 别名]
def init_deap_functions(self):

        creator.create("Fitness", base.Fitness, weights=self.weights)
        creator.create("Individual", list, fitness=creator.Fitness)

        self.toolbox = base.Toolbox()

        self.toolbox.register("individual", tools.initIterate, creator.Individual, self.generate_ind)
        self.toolbox.register("population", tools.initRepeat, list, self.toolbox.individual)

        self.toolbox.register("evaluate", self.fit_func)

        if self.penalty != None:
            self.toolbox.decorate("evaluate", tools.DeltaPenality(self.feasible, self.inf_val)) 

# 需要导入模块: from deap import base [as 别名]
# 或者: from deap.base import Fitness [as 别名]
def create_toolbox(self):
    """OptiGenAlgNsga2Deap method to create DEAP toolbox
    Parameters
    ----------
    self : OptiGenAlgNsga2Deap

    Returns
    -------
    self : OptiGenAlgNsga2Deap
        OptiGenAlgNsga2Deap with toolbox created 
    """

    # Create toolbox
    self.toolbox = base.Toolbox()

    # Create Fitness and individual
    creator.create(
        "FitnessMin", base.Fitness, weights=[-1 for _ in self.problem.design_var]
    )
    creator.create("Individual", list, typecode="d", fitness=creator.FitnessMin)

    self.toolbox.register("creator", creator.Individual)

    # Register individual and population
    self.toolbox.register(
        "individual",
        create_indiv,
        self.toolbox.creator,
        self.problem.output,
        self.problem.design_var,
    )

    self.toolbox.register("population", tools.initRepeat, list, self.toolbox.individual) 

# 需要导入模块: from deap import base [as 别名]
# 或者: from deap.base import Fitness [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 base [as 别名]
# 或者: from deap.base import Fitness [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 elitism:
    population, logbook = elitism.eaSimpleWithElitism(population, toolbox, cxpb=P_CROSSOVER, mutpb=P_MUTATION,
                                              ngen=MAX_GENERATIONS, stats=stats, halloffame=hof, verbose=True)

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

    # extract statistics:
    minFitnessValues, meanFitnessValues = logbook.select("min", "avg")

    # plot statistics:
    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')

    plt.show() 

# 需要导入模块: from deap import base [as 别名]
# 或者: from deap.base import Fitness [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 = elitism.eaSimpleWithElitism(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 base [as 别名]
# 或者: from deap.base import Fitness [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 base [as 别名]
# 或者: from deap.base import Fitness [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 = elitism.eaSimpleWithElitism(population,
                                                      toolbox,
                                                      cxpb=P_CROSSOVER,
                                                      mutpb=P_MUTATION,
                                                      ngen=MAX_GENERATIONS,
                                                      stats=stats,
                                                      halloffame=hof,
                                                      verbose=True)

    # print best solution found:
    print("- Best solution is: ")
    print("params = ", test.formatParams(hof.items[0]))
    print("Accuracy = %1.5f" % hof.items[0].fitness.values[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 base [as 别名]
# 或者: from deap.base import Fitness [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", numpy.min)
    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 = elitism.eaSimpleWithElitism(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 Individual = ", best)
    print("-- Best Fitness = ", best.fitness.values[0])
    print()
    print("-- Schedule = ")
    nsp.printScheduleInfo(best)

    # extract statistics:
    minFitnessValues, meanFitnessValues = logbook.select("min", "avg")

    # plot statistics:
    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')
    plt.show() 

# 需要导入模块: from deap import base [as 别名]
# 或者: from deap.base import Fitness [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 = elitism.eaSimpleWithElitism(population, toolbox, cxpb=P_CROSSOVER, mutpb=P_MUTATION,
                                              ngen=MAX_GENERATIONS, stats=stats, halloffame=hof, verbose=True)

    # print hall of fame members info:
    print("- Best solutions are:")
    for i in range(HALL_OF_FAME_SIZE):
        print(i, ": ", hof.items[i].fitness.values[0], " -> ", hof.items[i])

    # plot statistics:
    minFitnessValues, meanFitnessValues = logbook.select("min", "avg")
    plt.figure(1)
    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')

    # plot best solution:
    sns.set_style("whitegrid", {'axes.grid' : False})
    nQueens.plotBoard(hof.items[0])

    # show both plots:
    plt.show() 

# 需要导入模块: from deap import base [as 别名]
# 或者: from deap.base import Fitness [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", numpy.min)
    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 = elitism.eaSimpleWithElitism(population,
                                                      toolbox,
                                                      cxpb=P_CROSSOVER,
                                                      mutpb=P_MUTATION,
                                                      ngen=MAX_GENERATIONS,
                                                      stats=stats,
                                                      halloffame=hof,
                                                      verbose=True)

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

    # save best solution for a replay:
    car.saveActions(best) 

# 需要导入模块: from deap import base [as 别名]
# 或者: from deap.base import Fitness [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 base [as 别名]
# 或者: from deap.base import Fitness [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", numpy.min)
    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 = elitism.eaSimpleWithElitism(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])

    # extract statistics:
    minFitnessValues, meanFitnessValues = logbook.select("min", "avg")

    # plot statistics:
    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')
    plt.show()