def __init__(self, *args, **kwargs):
MemeticSearch.__init__(self, *args, **kwargs)
# lambada must be mu-ltiple of mu
assert self.lambada % self.mu == 0
# population is a list of (fitness, individual) tuples.
self.population = [(self.bestEvaluation, self.bestEvaluable.copy())]
for dummy in range(1, self.mu + self.lambada):
x = self.bestEvaluable.copy()
x.mutate()
self.population.append((self.evaluator(x), x))
self._sortPopulation()
self.steps = self.mu + self.lambada
def _learnStep(self):
self.switchMutations()
MemeticSearch._learnStep(self)
self.switchMutations()
def _learnStep(self):
self.switchMutations()
MemeticSearch._learnStep(self)
self.switchMutations()