def NSGA2(name):
model = DimacsModel(name)
toolbox = model.toolbox
toolbox.register('mutate', model.bit_flip_mutate)
toolbox.register('select', tools.selNSGA2)
toolbox.register('mate', model.cxTwoPoint)
# get the initial generation from the result of sway
# and evaluate them
pop = load_sway_results(model)
toolbox.map(model.eval_ind, pop)
# the parameters for NSGA-II as follows
MU = 100
NGEN = 100
CXPB = 0.9
# start the NSGA2 algorithms
for _ in range(100-len(pop)):
pop.append(random.choice(pop))
random.shuffle(pop)
for gen in range(1, NGEN):
# vary the population
tools.emo.assignCrowdingDist(pop)
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
map(model.eval, offspring)
# Select the next generation population
pop = toolbox.select(pop + offspring, MU)
valid_pop = [i for i in pop if i.fitness.values[0] <=0]
hv, spread, _, size, _ = stat_basing_on_pop(valid_pop, False)
print(hv, spread, size)
return hv, spread, size
def IBEA(name):
model = DimacsModel(name)
toolbox = model.toolbox
toolbox.register('mutate', model.bit_flip_mutate)
toolbox.register('select', tools.selIBEA)
toolbox.register('mate', model.cxTwoPoint)
toolbox.register('clone', copy.deepcopy)
# get the initial generation from the result of sway
# and evaluate them
pop = load_sway_results(model)
toolbox.map(model.eval_ind, pop)
# the parameters for IBEA as follows
MU = 100
NGEN = 100
CXPB = 0.9
# start the NSGA2 algorithms
for _ in range(100-len(pop)):
pop.append(random.choice(pop))
random.shuffle(pop)
parents = pop[:]
for gen in range(1, NGEN):
# Vary the parents
offspring = varAnd(parents, toolbox, CXPB, 0.2)
pop[:] = parents + offspring
for p in pop:
if not p.fitness.valid:
model.eval(p)
# Select the next generation parents
parents[:] = toolbox.select(pop, MU)
# pdb.set_trace()
valid_pop = [i for i in pop if i.fitness.values[0] <=0]
hv, spread, _, size, _ = stat_basing_on_pop(valid_pop, False)
print(hv, spread, size)
return hv, spread, size
