def nsga2select(population, fitnesses, survivors, allowequality = True):
"""The NSGA-II selection strategy (Deb et al., 2002).
The number of individuals that survive is given by the survivors parameter."""
fronts = non_dominated_sort(population,
key=lambda x: fitnesses[x],
allowequality = allowequality)
individuals = set()
for front in fronts:
remaining = survivors - len(individuals)
if not remaining > 0:
break
if len(front) > remaining:
# If the current front does not fit in the spots left, use those
# that have the biggest crowding distance.
crowd_dist = crowding_distance(front, fitnesses)
front = sorted(front, key=lambda x: crowd_dist[x], reverse=True)
front = set(front[:remaining])
individuals |= front
return list(individuals)
def nsga2select(population, fitnesses, survivors, allowequality=True):
"""The NSGA-II selection strategy (Deb et al., 2002).
The number of individuals that survive is given by the survivors parameter."""
fronts = non_dominated_sort(population,
key=lambda x: fitnesses[x],
allowequality=allowequality)
individuals = set()
for front in fronts:
remaining = survivors - len(individuals)
if not remaining > 0:
break
if len(front) > remaining:
# If the current front does not fit in the spots left, use those
# that have the biggest crowding distance.
crowd_dist = crowding_distance(front, fitnesses)
front = sorted(front, key=lambda x: crowd_dist[x], reverse=True)
front = set(front[:remaining])
individuals |= front
return list(individuals)