def selNSGA2(problem, population, selectees, configurations):
k = configurations["Universal"]["Population_Size"]
# Evaluate any new guys
for individual in population + selectees:
if not individual.valid:
individual.evaluate()
# Format a population Data structure usable by DEAP's package
dIndividuals = deap_format(problem, population + selectees)
# Combine
from Algorithms.DEAP.tools.emo import deap_selNSGA2
dIndividuals = deap_selNSGA2(dIndividuals, k)
# Copy from DEAP structure to JMOO structure
population = []
for i, dIndividual in enumerate(dIndividuals):
cells = []
for j in range(len(dIndividual)):
cells.append(dIndividual[j])
population.append(
jmoo_individual(problem, cells,
[f for f in dIndividual.fitness.values]))
return population, k
def selNSGA2(problem, population, selectees, configurations):
k = configurations["Universal"]["Population_Size"]
# Evaluate any new guys
for individual in population + selectees:
if not individual.valid:
individual.evaluate()
# Format a population Data structure usable by DEAP's package
dIndividuals = deap_format(problem, population + selectees)
# Combine
from Algorithms.DEAP.tools.emo import deap_selNSGA2
dIndividuals = deap_selNSGA2(dIndividuals, k)
# Copy from DEAP structure to JMOO structure
population = []
for i, dIndividual in enumerate(dIndividuals):
cells = []
for j in range(len(dIndividual)):
cells.append(dIndividual[j])
population.append(jmoo_individual(problem, cells, [f for f in dIndividual.fitness.values]))
return population, k
def get_non_dominated_solutions(problem, population, configurations):
# NOTE: This might look wierd but this would return all the non dominated solutions
k = configurations["Universal"]["Population_Size"]
# Evaluate any new guys
for individual in population:
if not individual.valid:
individual.evaluate()
# Format a population Data structure usable by DEAP's package
dIndividuals = deap_format(problem, population)
# Combine
from Algorithms.DEAP.tools.emo import deap_selNSGA2
dIndividuals = deap_selNSGA2(dIndividuals, k)
# Copy from DEAP structure to JMOO structure
population = []
for i, dIndividual in enumerate(dIndividuals):
cells = []
for j in range(len(dIndividual)):
cells.append(dIndividual[j])
population.append(jmoo_individual(problem, cells, [f for f in dIndividual.fitness.values]))
return population
