def compute(self):
# init toolbox
toolbox = base.Toolbox()
configuration_executor.execute(self.configuration, toolbox, self.comp_prop)
self.set_globals()
# toolbox.register("evaluate", self.f_problem)
# before tests, only rastrigin
toolbox.register("evaluate", self.problem_arg)
# init population
pop = toolbox.population(n=self.POP)
hof = tools.HallOfFame(maxsize=self.HOF_SIZE)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", tools.mean)
stats.register("std", tools.std)
stats.register("min", min)
stats.register("max", max)
population, partial_res = alg_helper.eaSimple(pop, toolbox, cxpb=0.8, mutpb=1, ngen=self.GEN,monitoring=self.monitoring,
stats=stats, halloffame=hof, verbose=True)
print "Best individual: " + str(self.convertArrToFloat(hof[0]))
print "Rastrigin value: " + str(hof[0].fitness.values) + "\n\n\n"
sorted_individuals = [self.convertArrToFloat(ind) for ind in hof]
fitness_values = [ind.fitness.values[0] for ind in hof]
stats_results = [stats.data[name][0][self.GEN][-1] for name in stats.functions]
stats_part_results = [[stats.data[name][0][i][-1] for name in stats.functions] for i in xrange(self.GEN)]
return sorted_individuals,fitness_values, partial_res, stats_results, stats_part_results
def computePipes(computation, algorithm, args):
NBR_DEMES = 3 # hardcoded for the moment because VM has only 3 threads
pipes = [Pipe(True) for _ in range(NBR_DEMES)]
pipes_in = deque(p[0] for p in pipes)
pipes_out = deque(p[1] for p in pipes)
pipes_in.rotate(1)
pipes_out.rotate(-1)
results = list()
queue = Queue()
for iter in xrange(computation['repeat']):
algs = list()
processes = list()
# in order to create all names in module, so deserialization runs painlessly
configuration_executor.execute(args['configuration'], base.Toolbox(), dict())
for deme in xrange(NBR_DEMES):
args['rank'] = (pipes_in[deme], pipes_out[deme], queue)
algs.append(eval(algorithm)(**args))
processes.append(Process(target=algs[-1].compute))
for proc in processes:
proc.start()
for deme in xrange(NBR_DEMES):
results.append(queue.get())
for proc in processes:
proc.join()
return results
def parse_and_execute_configuration(self):
configuration_executor.execute(self.configuration, self.toolbox, self.comp_prop)
