def evalEvoSN(individual, dimension):
network = sn.SortingNetwork(dimension, individual)
return network.assess(), network.length, network.depth
def main():
random.seed(64)
population = toolbox.population(n=300)
hof = tools.ParetoFront()
stats = tools.Statistics(lambda ind: ind.fitness.values)
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", "std", "min", "avg", "max"
CXPB, MUTPB, ADDPB, DELPB, NGEN = 0.5, 0.2, 0.01, 0.01, 40
# Evaluate every individuals
fitnesses = toolbox.map(toolbox.evaluate, population)
for ind, fit in zip(population, fitnesses):
ind.fitness.values = fit
hof.update(population)
record = stats.compile(population)
logbook.record(gen=0, evals=len(population), **record)
print(logbook.stream)
# Begin the evolution
for g in range(1, NGEN):
offspring = [toolbox.clone(ind) for ind in population]
# Apply crossover and mutation
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() < CXPB:
toolbox.mate(ind1, ind2)
del ind1.fitness.values
del ind2.fitness.values
# Note here that we have a different sheme of mutation than in the
# original algorithm, we use 3 different mutations subsequently.
for ind in offspring:
if random.random() < MUTPB:
toolbox.mutate(ind)
del ind.fitness.values
if random.random() < ADDPB:
toolbox.addwire(ind)
del ind.fitness.values
if random.random() < DELPB:
toolbox.delwire(ind)
del ind.fitness.values
# Evaluate the individuals with an invalid fitness
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
population = toolbox.select(population + offspring, len(offspring))
hof.update(population)
record = stats.compile(population)
logbook.record(gen=g, evals=len(invalid_ind), **record)
print(logbook.stream)
best_network = sn.SortingNetwork(INPUTS, hof[0])
print(stats)
print(best_network)
print(best_network.draw())
print("%i errors, length %i, depth %i" % hof[0].fitness.values)
return population, logbook, hof
def main():
# test if file is ok before starting the test
if args.filename:
open(args.filename).close()
random.seed(64)
beginTime = time.time()
evaluationTime = 0
population = toolbox.population(n=args.population)
hof = tools.ParetoFront()
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)
logger = tools.EvolutionLogger(["gen", "evals", "time"] +
[str(k) for k in stats.functions.keys()])
logger.logHeader()
CXPB, MUTPB, ADDPB, DELPB, NGEN = 0.5, 0.2, 0.01, 0.01, args.generations
evalBegin = time.time()
# Evaluate every individuals
fitnesses = toolbox.map(toolbox.evaluate, population)
for ind, fit in zip(population, fitnesses):
ind.fitness.values = fit
evaluationTime += (time.time() - evalBegin)
hof.update(population)
stats.update(population)
logger.logGeneration(gen=0,
evals=len(population),
stats=stats,
time=evaluationTime)
# Begin the evolution
for g in range(1, NGEN):
offspring = [toolbox.clone(ind) for ind in population]
# Apply crossover and mutation
for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
if random.random() < CXPB:
toolbox.mate(ind1, ind2)
del ind1.fitness.values
del ind2.fitness.values
# Note here that we have a different sheme of mutation than in the
# original algorithm, we use 3 different mutations subsequently.
for ind in offspring:
if random.random() < MUTPB:
toolbox.mutate(ind)
del ind.fitness.values
if random.random() < ADDPB:
toolbox.addwire(ind)
del ind.fitness.values
if random.random() < DELPB:
toolbox.delwire(ind)
del ind.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
evalBegin = time.time()
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
evaluationTime += (time.time() - evalBegin)
population = toolbox.select(population + offspring, len(offspring))
hof.update(population)
stats.update(population)
logger.logGeneration(gen=g,
evals=len(invalid_ind),
stats=stats,
time=evaluationTime)
best_network = sn.SortingNetwork(INPUTS, hof[0])
print(best_network)
print(best_network.draw())
print("%i errors, length %i, depth %i" % hof[0].fitness.values)
totalTime = time.time() - beginTime
print("Total time: {0}\nEvaluation time: {1}".format(
totalTime, evaluationTime))
if args.filename:
f = open(args.filename, "a")
f.write("{0};{1};{2};{3}\n".format(args.cores, INPUTS, totalTime,
evaluationTime))
f.close()
return population, stats, hof