def initialize(config):
pop = getToolBox(config).population(n=config["POPULATION_SIZE"])
print pop
server = evospace.Population("pop")#jsonrpclib.Server(config["SERVER"])
server.initialize()
#server.initialize(None)
sample = [{"chromosome":ind[:], "id":None, "fitness":{"DefaultContext":0.0}} for ind in pop]
init_pop = {'sample_id': 'None' , 'sample': sample}
server.put_sample(init_pop)
def work(params):
worker_id = params[0][0]
config = params[0][1]
#server = jsonrpclib.Server(config["SERVER"])
server = evospace.Population("pop")
results = []
for sample_num in range(config["MAX_SAMPLES"]):
#if int(server.found(None)):
if int(server.found()):
print server.found()
break
else:
gen_data = evolve(sample_num, config)
if gen_data[0]:
server.found()
results.append([worker_id] + gen_data[1])
return results
def __init__(self, popName="pop"):
self.population = evospace.Population(popName)
self.population.initialize()
def evolve(sample_num, config):
#random.seed(64)
toolbox = getToolBox(config)
start = time.time()
server = evospace.Population("pop")
#server = jsonrpclib.Server(config["SERVER"])
evospace_sample = server.get_sample(config["SAMPLE_SIZE"])
#evospace_sample = server.getSample(config["SAMPLE_SIZE"])
pop = [ creator.Individual(cs['chromosome']) for cs in evospace_sample['sample']]
begin = time.time()
# Evaluate the entire population
fitnesses = map(toolbox.evaluate, pop)
for ind, fit in zip(pop, fitnesses):
ind.fitness.values = fit
total_evals = len(pop)
best_first = None
# Begin the evolution
for g in range(config["WORKER_GENERATIONS"]):
# Select the next generation individuals
offspring = toolbox.select(pop, len(pop))
# Clone the selected individuals
offspring = map(toolbox.clone, offspring)
# Apply crossover and mutation on the offspring
for child1, child2 in zip(offspring[::2], offspring[1::2]):
if random.random() < config["CXPB"]:
toolbox.mate(child1, child2)
del child1.fitness.values
del child2.fitness.values
for mutant in offspring:
if random.random() < config["MUTPB"]:
toolbox.mutate(mutant)
del mutant.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
total_evals+=len(invalid_ind)
#print " Evaluated %i individuals" % len(invalid_ind),
# The population is entirely replaced by the offspring
pop[:] = offspring
# Gather all the fitnesses in one list and print the stats
fits = [ind.fitness.values[0] for ind in pop]
#length = len(pop)
#mean = sum(fits) / length
#sum2 = sum(x*x for x in fits)
#std = abs(sum2 / length - mean**2)**0.5
best = max(fits)
if not best_first:
best_first = best
if best >= config["CHROMOSOME_LENGTH"]:
break
#print " Min %s" % min(fits) + " Max %s" % max(fits)+ " Avg %s" % mean + " Std %s" % std
print "-- End of (successful) evolution --"
putback = time.time()
sample = [ {"chromosome":ind[:],"id":None,
"fitness":{"DefaultContext":ind.fitness.values[0]} }
for ind in pop]
evospace_sample['sample'] = sample
server.put_sample(evospace_sample)
#server.putSample(evospace_sample)
best_ind = tools.selBest(pop, 1)[0]
return best >= config["CHROMOSOME_LENGTH"] , [config["CHROMOSOME_LENGTH"],best, sample_num, round(time.time() - start, 2),
round(begin - start, 2), round(putback - begin, 2),
round(time.time() - putback, 2), total_evals, best_first, best_ind]