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tournament_selection.m
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tournament_selection.m
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function f = tournament_selection(chromosome, pool_size, tour_size)
%% function tournament_selection(chromosome, pool_size, tour_size)
% is the selection policy for selecting the individuals for the mating
% pool. The selection is based on tournament selection. Argument
% |chromosome| is the current generation population from which the
% individuals are selected to form a mating pool of size |pool_size| after
% performing tournament selection, with size of the tournament being
% |tour_size|. By varying the tournament size the selection pressure can be
% adjusted. But for NSGA-II the tour_size is fixed to two, but the user may
% feel free to experiment with different tournament size. Also it has been
% observed that a tournament size of more than five has no significant
% meaning.
%
%% Tournament selection process
% In a tournament selection process n individuals are selected at random,
% where n is equal to |tour_size|. From these individuals only one is selected
% and is added to the mating pool, where size of the mating pool is
% |pool_size|. Selection is performed based on two criteria. First and
% foremost is the rank or the front in which the solutions reside.
% Individuals with lower rank are selected. Secondly if the rank of two
% individuals are the same then, the crowding distance is compared.
% Individuals with greater crowding distance is selcted.
% Copyright (c) 2009, Aravind Seshadri
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are
% met:
%
% * Redistributions of source code must retain the above copyright
% notice, this list of conditions and the following disclaimer.
% * Redistributions in binary form must reproduce the above copyright
% notice, this list of conditions and the following disclaimer in
% the documentation and/or other materials provided with the distribution
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
% POSSIBILITY OF SUCH DAMAGE.
% Get the size of chromosome. The number of chromosome is not important
% while the number of elements in chromosome are important.
[pop, variables] = size(chromosome);
% The peunltimate element contains the information about rank.
rank = variables - 1;
% The last element contains information about crowding distance.
distance = variables;
% Until the mating pool is filled, perform tournament selection
for i = 1 : pool_size
% Select n individuals at random, where n = tour_size
for j = 1 : tour_size
% Select an individual at random
candidate(j) = round(pop*rand(1));
% Make sure that the array starts from one.
if candidate(j) == 0
candidate(j) = 1;
end
if j > 1
% Make sure that same candidate is not choosen.
while ~isempty(find(candidate(1 : j - 1) == candidate(j)))
candidate(j) = round(pop*rand(1));
if candidate(j) == 0
candidate(j) = 1;
end
end
end
end
% Collect information about the selected candidates.
for j = 1 : tour_size
c_obj_rank(j) = chromosome(candidate(j),rank);
c_obj_distance(j) = chromosome(candidate(j),distance);
end
% Find the candidate with the least rank
min_candidate = ...
find(c_obj_rank == min(c_obj_rank));
% If more than one candiate have the least rank then find the candidate
% within that group having the maximum crowding distance.
if length(min_candidate) ~= 1
max_candidate = ...
find(c_obj_distance(min_candidate) == max(c_obj_distance(min_candidate)));
% If a few individuals have the least rank and have maximum crowding
% distance, select only one individual (not at random).
if length(max_candidate) ~= 1
max_candidate = max_candidate(1);
end
% Add the selected individual to the mating pool
f(i,:) = chromosome(candidate(min_candidate(max_candidate)),:);
else
% Add the selected individual to the mating pool
f(i,:) = chromosome(candidate(min_candidate(1)),:);
end
end