Source code for surrogate.selection.selDoubleTournament

# Copyright 2016 Quan Pan
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
# Author: Quan Pan <>
# License: Apache License, Version 2.0
# Create: 2016-12-02

import random
from functools import partial
from operator import attrgetter

from .selRandom import selRandom

[docs]def selDoubleTournament(individuals, k=2, fitness_size=1, parsimony_size=1, fitness_first=True): """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. # TODO 20161205 check the return value # ind1, ind2 = select(individuals, k=2) ind1 = select(individuals, k=2) 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)