def _do(self, pop, off, size, return_sorted_idx=False, out=None, **kwargs):
if off is not None:
pop.merge(off)
fronts = NonDominatedRank.calc_as_fronts(pop.F, pop.G)
rank = NonDominatedRank.calc_from_fronts(fronts)
crowding = np.zeros(pop.F.shape[0])
for front in fronts:
cd_of_front = RankAndCrowdingSurvival.calc_crowding_distance(pop.F[front, :])
crowding[front] = cd_of_front
sorted_idx = sorted(range(pop.size()), key=lambda x: (rank[x], -crowding[x]))
if return_sorted_idx:
return sorted_idx
# now truncate the population
sorted_idx = sorted_idx[:size]
pop.filter(sorted_idx)
rank = rank[sorted_idx]
crowding = crowding[sorted_idx]
if out is not None:
out['rank'] = rank
out['crowding'] = crowding
return pop
def _do(self, pop, size, data, return_sorted_idx=False):
fronts = NonDominatedRank.calc_as_fronts(pop.F, pop.G)
rank = NonDominatedRank.calc_from_fronts(fronts)
crowding = np.zeros(pop.F.shape[0])
for front in fronts:
cd_of_front = RankAndCrowdingSurvival.calc_crowding_distance(
pop.F[front, :])
crowding[front] = cd_of_front
sorted_idx = sorted(range(pop.size()),
key=lambda x: (rank[x], -crowding[x]))
if return_sorted_idx:
return sorted_idx
# now truncate the population
sorted_idx = sorted_idx[:size]
pop.filter(sorted_idx)
rank = rank[sorted_idx]
crowding = crowding[sorted_idx]
if data is not None:
data.rank = rank
data.crowding = crowding
return pop