from __future__ import division import random from functools import partial from operator import attrgetter ###################################### # Selections # ###################################### def selRandom(individuals, k): """Select *k* individuals at random from the input *individuals* with replacement. The list returned contains references to the input *individuals*. :param individuals: A list of individuals to select from. :param k: The number of individuals to select. :returns: A list of selected individuals. This function uses the :func:`~random.choice` function from the python base :mod:`random` module. """ return [random.choice(individuals) for i in xrange(k)] def selBest(individuals, k): """Select the *k* best individuals among the input *individuals*. The list returned contains references to the input *individuals*. :param individuals: A list of individuals to select from. :param k: The number of individuals to select. :returns: A list containing the k best individuals. """ return sorted(individuals, key=attrgetter("fitness"), reverse=True)[:k] def selWorst(individuals, k): """Select the *k* worst individuals among the input *individuals*. The list returned contains references to the input *individuals*. :param individuals: A list of individuals to select from. :param k: The number of individuals to select. :returns: A list containing the k worst individuals. """ return sorted(individuals, key=attrgetter("fitness"))[:k] def selTournament(individuals, k, tournsize): """Select *k* individuals from the input *individuals* using *k* tournaments of *tournsize* individuals. The list returned contains references to the input *individuals*. :param individuals: A list of individuals to select from. :param k: The number of individuals to select. :param tournsize: The number of individuals participating in each tournament. :returns: A list of selected individuals. This function uses the :func:`~random.choice` function from the python base :mod:`random` module. """ chosen = [] for i in xrange(k): aspirants = selRandom(individuals, tournsize) chosen.append(max(aspirants, key=attrgetter("fitness"))) return chosen def selRoulette(individuals, k): """Select *k* individuals from the input *individuals* using *k* spins of a roulette. The selection is made by looking only at the first objective of each individual. The list returned contains references to the input *individuals*. :param individuals: A list of individuals to select from. :param k: The number of individuals to select. :returns: A list of selected individuals. This function uses the :func:`~random.random` function from the python base :mod:`random` module. .. warning:: The roulette selection by definition cannot be used for minimization or when the fitness can be smaller or equal to 0. """ s_inds = sorted(individuals, key=attrgetter("fitness"), reverse=True) sum_fits = sum(ind.fitness.values[0] for ind in individuals) chosen = [] for i in xrange(k): u = random.random() * sum_fits sum_ = 0 for ind in s_inds: sum_ += ind.fitness.values[0] if sum_ > u: chosen.append(ind) break return chosen def selDoubleTournament(individuals, k, fitness_size, parsimony_size, fitness_first): """Tournament selection which use the size of the individuals in order to discriminate good solutions. This kind of tournament is obviously useless with fixed-length representation, but has been shown to significantly reduce excessive growth of individuals, especially in GP, where it can be used as a bloat control technique (see [Luke2002fighting]_). This selection operator implements the double tournament technique presented in this paper. The core principle is to use a normal tournament selection, but using a special sample function to select aspirants, which is another tournament based on the size of the individuals. To ensure that the selection pressure is not too high, the size of the size tournament (the number of candidates evaluated) can be a real number between 1 and 2. In this case, the smaller individual among two will be selected with a probability *size_tourn_size*/2. For instance, if *size_tourn_size* is set to 1.4, then the smaller individual will have a 0.7 probability to be selected. .. note:: In GP, it has been shown that this operator produces better results when it is combined with some kind of a depth limit. :param individuals: A list of individuals to select from. :param k: The number of individuals to select. :param fitness_size: The number of individuals participating in each \ fitness tournament :param parsimony_size: The number of individuals participating in each \ size tournament. This value has to be a real number\ in the range [1,2], see above for details. :param fitness_first: Set this to True if the first tournament done should \ be the fitness one (i.e. the fitness tournament producing aspirants for \ the size tournament). Setting it to False will behaves as the opposite \ (size tournament feeding fitness tournaments with candidates). It has been \ shown that this parameter does not have a significant effect in most cases\ (see [Luke2002fighting]_). :returns: A list of selected individuals. .. [Luke2002fighting] Luke and Panait, 2002, Fighting bloat with nonparametric parsimony pressure """ assert (1 <= parsimony_size <= 2), "Parsimony tournament size has to be in the range [1, 2]." def _sizeTournament(individuals, k, select): chosen = [] for i in xrange(k): # Select two individuals from the population # The first individual has to be the shortest prob = parsimony_size / 2. ind1, ind2 = select(individuals, k=2) if len(ind1) > len(ind2): ind1, ind2 = ind2, ind1 elif len(ind1) == len(ind2): # random selection in case of a tie prob = 0.5 # Since size1 <= size2 then ind1 is selected # with a probability prob chosen.append(ind1 if random.random() < prob else ind2) return chosen def _fitTournament(individuals, k, select): chosen = [] for i in xrange(k): aspirants = select(individuals, k=fitness_size) chosen.append(max(aspirants, key=attrgetter("fitness"))) return chosen if fitness_first: tfit = partial(_fitTournament, select=selRandom) return _sizeTournament(individuals, k, tfit) else: tsize = partial(_sizeTournament, select=selRandom) return _fitTournament(individuals, k, tsize) __all__ = ['selRandom', 'selBest', 'selWorst', 'selRoulette', 'selTournament', 'selDoubleTournament']