我想使用基于python的框架DEAP来解决多目标优化问题。由于处理过程耗时，我需要使用我所有的CPU功率来进行计算。因此，我使用了DEAP文档中建议的多处理库——这个例子，但它产生了PicklingError: Can't pickle <type 'function'>: attribute lookup __builtin__.function failed。我的总代码太长了，无法写下来，但下面的代码与我的代码相似，导致了相同的错误。你能告诉我哪里出错吗？提前感谢
import multiprocessing
from deap import creator, base, tools, algorithms
import random
import matplotlib.pyplot as plt
def TEST(dec_var):
return dec_var[0]**2+dec_var[1]**2,(dec_var[0]-2)**2+dec_var[1]**2
def feasible(dec_var):
if all(i>0 for i in dec_var):
return True
return False
creator.create("FitnessMin", base.Fitness, weights=(-1.0,-1.0))
creator.create("Individual", list, fitness=creator.FitnessMin)
toolbox=base.Toolbox()
toolbox.register("uniform", random.uniform, 0.0, 7.0)
toolbox.register("individual",tools.initRepeat,creator.Individual,toolbox.uniform ,n=2)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.1)
toolbox.register("select", tools.selNSGA2)
toolbox.register("evaluate", TEST)
toolbox.decorate("evaluate", tools.DeltaPenalty(feasible,(1000,1000)))
def main(seed=None):
random.seed(seed)
NGEN = 250
MU = 100
CXPB = 0.9
stats_func1 = tools.Statistics(key=lambda ind: ind.fitness.values[0])
stats_func2 = tools.Statistics(key=lambda ind: ind.fitness.values[1])
stats = tools.MultiStatistics(func1=stats_func1, func2=stats_func2)
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", "func1","func2"
logbook.chapters["func1"].header = "min", "max"
logbook.chapters["func2"].header = "min", "max"
pop = toolbox.population(n=MU)
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
pop = toolbox.select(pop, len(pop))
record = stats.compile(pop)
logbook.record(gen=0, evals=len(invalid_ind), **record)
print(logbook.stream)
for gen in range(1, NGEN):
offspring = tools.selTournamentDCD(pop, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() <= CXPB:
toolbox.mate(ind1, ind2)
toolbox.mutate(ind1)
toolbox.mutate(ind2)
del ind1.fitness.values, ind2.fitness.values
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
pop = toolbox.select(pop + offspring, MU)
record = stats.compile(pop)
logbook.record(gen=gen, evals=len(invalid_ind), **record)
print(logbook.stream)
return pop, logbook
if __name__ == "__main__":
pool = multiprocessing.Pool(processes=4)
toolbox.register("map", pool.map)
pop, stats = main()
pool.close()
print pop
这在您链接的文档页面上有说明：
lambda函数的pickle在Python中还不可用。
您在stats_func1和stats_func2中使用lambda函数–只需将它们移到全局函数中，然后重试：
def stats_key_1(ind):
return ind.fitness.values[0]
def stats_key_2(ind):
return ind.fitness.values[1]
# ... snip ...
def main(seed=None):
# ... snip ...
stats_func1 = tools.Statistics(key=stats_key_1)
stats_func2 = tools.Statistics(key=stats_key_1)