Exemple #1
0
def main():
	# collectors queues: fetch waxman network and send best chromosome params
	waxqueue = Queue(1)
	bestqueue = Queue(maxsize=0)

	# Manages parallel communication between node and collector
	collector = CollectorCommunication(waxqueue, bestqueue)
	collector.start()

	# np.float64 array (NxN matrix) @waxnet: Waxman Network Graph (blockin call)
	collector.request = 'waxnet\n'
	(waxsource, waxtarget, waxnet) = waxqueue.get()
	waxqueue.join()

	if waxsource == waxtarget:
		print 'pga: fatal error: loop to itself tomar no seu cu'
		sys.exit(0)

	# applies several genetic operators on individuals
	env = Environment()

	# open parallel communication between node and its neighbours (migration)
	env.open_borders()

	sleep(3)

	# generates initial population with random but valid chromosomes
	print 'pga: init pop generating %d individuals' % info.SIZE_NODE_POPULATION
	population = [] # [ [[chrom], fit], [[chrom], fit], ..., [[chrom], fit] ]
	while len(population) < info.SIZE_NODE_POPULATION:
		allels = range(info.NUMBER_OF_WAXNODES)
		chromosome = env.make_chromosome(waxnet, waxsource, waxtarget, allels)
		individual = [chromosome, 0.0]
		if chromosome and individual not in population:
			population.append(individual)

# <GeneticAlgorithm> start the GA on this node
	print 'pga: entrando no GA loop'
	for generation in range(info.NUMBER_OF_GENERATIONS):
		# perform evaluation (fitness calculation)
		print 'pga: evaluating population'
		for ind in xrange(len(population)):
			fit = env.evaluate(population[ind][0], waxnet)
			population[ind][1] = fit

		# perform selection
		print 'pga: selecting parents'
		mating_pool = env.select(list(population), info.RATE_CROSSOVER)

		# perform crossover
		print 'pga: crossing parents'
		offspring = env.cross(mating_pool)
		if offspring:
			for child in offspring:
				population.pop()
				population.insert(0, [child, 0.0])

		# perform mutation
		print 'pga: mutating'
		for i in xrange(int(math.ceil(info.RATE_MUTATION*len(population)))):
			normal_ind = random.choice(population)
			trans_ind = env.mutate(normal_ind[0], waxnet)
			if trans_ind != normal_ind:
				population.remove(normal_ind)
				population.insert(0, [trans_ind, 0.0])

		# perform (im)migration by polling
		print 'pga: immigration receiving best from neighbour'
		foreign = env.immigrate()
		if foreign:
			population.append([foreign, 0.0])

		# rearrange sort pop
		print 'pga: rearranging: descendent sort by fitness'
		population.sort(key=itemgetter(1), reverse=True)
		#population.sort(key=itemgetter(1))

		# perform (e)migration of best individual
		print 'pga: emmigration: sending best chromosome (only) to neighbour'
		env.emigrate(population[0][0])

		# send best chromosome and its fitness to collector
		print 'pga: sending best chrom and fit to collector'
		collector.request = 'chromo\n'
		bestqueue.put((population[0][0], population[0][1]))
# </GeneticAlgorithm>

	# perform evaluation (fitness calculation)
	print 'pga: last evaluating population'
	for ind in xrange(len(population)):
		population[ind][1] = env.evaluate(population[ind][0], waxnet)

	# rearrange sort pop
	print 'pga: last rearranging'
	population.sort(key=itemgetter(1), reverse=True)

	print population[0]

	# TODO: Close all communications (sockets) of this node
	#collector.running = False

	# open parallel communication between node and its neighbours (migration)
	env.close_borders()

	# TODO: Close all parallel processing (threads) of this node
	bestqueue.join()
	collector.join()

	print 'best fit: ', population[0][1]
Exemple #2
0
def main():
	# collectors queues: fetch waxman network and send best chromosome params
	#waxqueue = Queue(1)
	#bestqueue = Queue(maxsize=0)

	## Manages parallel communication between node and collector
	#collector = CollectorCommunication(waxqueue, bestqueue)
	#collector.start()

	# np.float64 array (NxN matrix) @waxnet: Waxman Network Graph (blockin call)
	#collector.request = 'waxnet\n'
	waxnet = waxnet_generator()

	# applies several genetic operators on individuals
	env = Environment()

	# open parallel communication between node and its neighbours (migration)
	#env.open_borders()

	sleep(4)

	print 'pga: init population'
	# generates initial population with random but valid chromosomes
	population = env.init_population(waxnet)

	print 'pga: entrando no GA loop'
# <GeneticAlgorithm> start the GA on this node
	for generation in range(info.NUMBER_OF_GENERATIONS):
		print 'pga: calculating fitness'
		# perform evaluation (fitness calculation)
		fitnesses = env.evaluate(population, waxnet)

		print 'pga: selecting'
		# perform selection
		mating_pool = env.select(population, fitnesses)

		print 'pga: crossing'
		# perform crossover
		children = env.cross(mating_pool)

		print 'pga: mutating'
		# perform mutation
		mystique = env.mutate(population, waxnet)

		print 'pga: receiving best from neighbour'
		# perform (im)migration by polling
		foreign = env.immigrate()

		print 'pga: sorting, organizing and rearranging'
		# TODO BUG sort population and replace worst individuals with better ones
		population, fitnesses = env.rearrange(population, fitnesses,\
					children, mystique, foreign)

		#print 'pga: sending best to neighbour'
		## perform (e)migration of best individual
		#env.emigrate(population[0])

		#print 'pga: sending best to collector'
		## send best chromosome and its fitness to collector
		#collector.request = 'chromo\n'
		#print population[0], fitnesses[0]
		#bestqueue.put((population[0], fitnesses[0]))
# </GeneticAlgorithm>

	# perform evaluation (fitness calculation)
	fitnesses = env.evaluate(population, waxnet)

	# TODO: Close all communications (sockets) of this node
	#collector.running = False

	## open parallel communication between node and its neighbours (migration)
	#env.close_borders()

	## TODO: Close all parallel processing (threads) of this node
	#bestqueue.join()
	#collector.join()

	print 'best fit: ', fitnesses[0]