def __init__(self, *args, **kwargs):
""" x0 is assumed to be an array, but then converted to a tuple.
The algorithm returns all individuals in the Pareto-front (and their fitnesses). """
GA.__init__(self, *args, **kwargs)
self.bestEvaluation = []
self.bestEvaluable = [tuple(self.x0)]
self.fitnesses = {}
def __init__(self, *args, **kwargs):
""" x0 is assumed to be an array, but then converted to a tuple.
The algorithm returns all individuals in the Pareto-front (and their fitnesses). """
GA.__init__(self, *args, **kwargs)
self.bestEvaluation = []
self.bestEvaluable = [tuple(self.x0)]
self.fitnesses = {}
def mutated(self, indiv):
return tuple(GA.mutated(self,array(indiv)))
def initPopulation(self):
if self.startPop == None:
GA.initPopulation(self)
self.currentpop = map(tuple, self.currentpop)
else:
self.currentpop = self.startPop
def mutated(self, indiv):
return tuple(GA.mutated(self, array(indiv)))
def initPopulation(self):
if self.startPop == None:
GA.initPopulation(self)
self.currentpop = map(tuple, self.currentpop)
else:
self.currentpop = self.startPop
