Source code for surrogate.selection.selNSGA2

# 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
# 
#    http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# Author: Quan Pan <quanpan302@hotmail.com>
# License: Apache License, Version 2.0
# Create: 2016-12-02

from itertools import chain
from operator import attrgetter

from .utils import assignCrowdingDist
from ..sorting import sorNondominated, sorLogNondominated

def selNSGA2(individuals, k, nd='standard'):
    """Apply NSGA-II selection operator on the *individuals*. Usually, the
    size of *individuals* will be larger than *k* because any individual
    present in *individuals* will appear in the returned list at most once.
    Having the size of *individuals* equals to *k* will have no effect other
    than sorting the population according to their front rank. The
    list returned contains references to the input *individuals*. For more
    details on the NSGA-II operator see [Deb2002]_.

    :param individuals: A list of individuals to select from.
    :param k: The number of individuals to select.
    :param nd: Specify the non-dominated algorithm to use: 'standard' or 'log'.
    :returns: A list of selected individuals.

    .. [Deb2002] Deb, Pratab, Agarwal, and Meyarivan, "A fast elitist
       non-dominated sorting genetic algorithm for multi-objective
       optimization: NSGA-II", 2002.
    """
    if nd == 'standard':
        pareto_fronts = sorNondominated(individuals, k)
    elif nd == 'log':
        pareto_fronts = sorLogNondominated(individuals, k)
    else:
        raise Exception('selNSGA2: The choice of non-dominated sorting '
                        'method "{0}" is invalid.'.format(nd))

    for front in pareto_fronts:
        assignCrowdingDist(front)

    chosen = list(chain(*pareto_fronts[:-1]))
    k = k - len(chosen)
    if k > 0:
        sorted_front = sorted(pareto_fronts[-1], key=attrgetter("fitness.crowding_dist"), reverse=True)
        chosen.extend(sorted_front[:k])

    return chosen