def __init__(self, chromosome, maxNIndividuals, valueInitializer=Randomization(-0.1,0.1), **kwargs):
""" @param chromosome: The prototype chromosome
@param maxNIndividuals: The maximum allowed number of individuals
"""
SimplePopulation.__init__(self)
self._prototype = EvolinoSubIndividual(chromosome)
self._maxNIndividuals = maxNIndividuals
self._valueInitializer = valueInitializer
self.setArgs(**kwargs)
for _ in range(maxNIndividuals):
self.addIndividual(self._prototype.copy())
self._valueInitializer.apply(self)
def apply(self, population):
""" First determines the number of individuals to be created.
Then clones the fittest individuals (=parents), mutates these clones
and adds them to the population.
"""
max_n = population.getMaxNIndividuals()
n = population.getIndividualsN()
freespace = max_n - n
best = population.getBestIndividualsSorted(freespace)
children=set()
while True:
if len(children)>=freespace: break
for parent in best:
children.add( parent.copy() )
if len(children)>=freespace: break
dummy_population = SimplePopulation()
dummy_population.addIndividuals(children)
self.mutation.apply(dummy_population)
population.addIndividuals(dummy_population.getIndividuals())
assert population.getMaxNIndividuals() == population.getIndividualsN()
