-
Notifications
You must be signed in to change notification settings - Fork 20
/
nonUnifMutation.m
45 lines (42 loc) · 2.14 KB
/
nonUnifMutation.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
function [parent] = nonUnifMutate(parent,bounds,Ops)
% Non uniform mutation changes one of the parameters of the parent
% based on a non-uniform probability distribution. This Gaussian
% distribution starts wide, and narrows to a point distribution as the
% current generation approaches the maximum generation.
%
% function [newSol] = multiNonUnifMutate(parent,bounds,Ops)
% parent - the first parent ( [solution string function value] )
% bounds - the bounds matrix for the solution space
% Ops - Options for nonUnifMutate[gen #NonUnifMutations maxGen b]
% Binary and Real-Valued Simulation Evolution for Matlab
% Copyright (C) 1996 C.R. Houck, J.A. Joines, M.G. Kay
%
% C.R. Houck, J.Joines, and M.Kay. A genetic algorithm for function
% optimization: A Matlab implementation. ACM Transactions on Mathmatical
% Software, Submitted 1996.
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either version 1, or (at your option)
% any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details. A copy of the GNU
% General Public License can be obtained from the
% Free Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
cg=Ops(1); % Current Generation
mg=Ops(3); % Maximum Number of Generations
b=Ops(4); % Shape parameter
df = bounds(:,2) - bounds(:,1); % Range of the variables
numVar = size(parent,2)-1; % Get the number of variables
% Pick a variable to mutate randomly from 1 to number of vars
mPoint = round(rand * (numVar-1)) + 1;
md = round(rand); % Choose a direction of mutation
if md % Mutate towards upper bound
newValue=parent(mPoint)+delta(cg,mg,bounds(mPoint,2)-parent(mPoint),b);
else % Mutate towards lower bound
newValue=parent(mPoint)-delta(cg,mg,parent(mPoint)-bounds(mPoint,1),b);
end
parent(mPoint) = newValue; % Make the child