def _learnStep(self):
""" do one generation step """
# evaluate fitness
""" added by JPQ """
if isinstance(self.fitnesses,dict):
oldfitnesses = self.fitnesses
self.fitnesses = dict()
for indiv in self.currentpop:
if tuple(indiv) in oldfitnesses:
self.fitnesses[tuple(indiv)] = oldfitnesses[tuple(indiv)]
else:
self.fitnesses[tuple(indiv)] = self._oneEvaluation(indiv)
del oldfitnesses
else:
# ---
self.fitnesses = dict([(tuple(indiv), self._oneEvaluation(indiv)) for indiv in self.currentpop])
if self.storeAllPopulations:
self._allGenerations.append((self.currentpop, self.fitnesses))
if self.elitism:
self.bestEvaluable = list(non_dominated_front(list(map(tuple, self.currentpop)),
key=lambda x: self.fitnesses[x],
allowequality = self.allowEquality))
else:
self.bestEvaluable = list(non_dominated_front(list(map(tuple, self.currentpop))+self.bestEvaluable,
key=lambda x: self.fitnesses[x],
allowequality = self.allowEquality))
self.bestEvaluation = [self.fitnesses[indiv] for indiv in self.bestEvaluable]
self.produceOffspring()
def _learnStep(self):
""" do one generation step """
# evaluate fitness
self.fitnesses = dict([(tuple(indiv), self._oneEvaluation(indiv)) for indiv in self.currentpop])
if self.storeAllPopulations:
self._allGenerations.append((self.currentpop, self.fitnesses))
if self.elitism:
self.bestEvaluable = list(non_dominated_front(map(tuple, self.currentpop),
key=lambda x: self.fitnesses[x],
allowequality = self.allowEquality))
else:
self.bestEvaluable = list(non_dominated_front(map(tuple, self.currentpop)+self.bestEvaluable,
key=lambda x: self.fitnesses[x],
allowequality = self.allowEquality))
self.bestEvaluation = [self.fitnesses[indiv] for indiv in self.bestEvaluable]
self.produceOffspring()
def _learnStep(self):
""" do one generation step """
# evaluate fitness
self.fitnesses = dict([(tuple(indiv), self._oneEvaluation(indiv))
for indiv in self.currentpop])
if self.storeAllPopulations:
self._allGenerations.append((self.currentpop, self.fitnesses))
if self.elitism:
self.bestEvaluable = list(
non_dominated_front(map(tuple, self.currentpop),
key=lambda x: self.fitnesses[x],
allowequality=self.allowEquality))
else:
self.bestEvaluable = list(
non_dominated_front(map(tuple, self.currentpop) +
self.bestEvaluable,
key=lambda x: self.fitnesses[x],
allowequality=self.allowEquality))
self.bestEvaluation = [
self.fitnesses[indiv] for indiv in self.bestEvaluable
]
self.produceOffspring()
