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Speeding Up Optimization Problems with Parallel Computing

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Speeding Up Optimization Problems with Parallel Computing


Stuart Kozola (view profile)


Files from the webinar: Speeding up optimization problems with parallel computing

Editor's Notes:

This file was selected as MATLAB Central Pick of the Week

function mutationChildren = mutationNcK(parents,options,GenomeLength,FitnessFcn,state,thisScore,thisPopulation)
% Oren Rosen
% 4/1/2008
% Copyright 2008 The MathWorks, Inc.
% This function is used for the N-choose-K bit-string genetic algorithm.
% The mutation algorithm is written to work on a population of length N
% bitstrings, each with exactly K bits equal to one. The children that are
% produced are simply random permutations of the parents. Because of this,
% the "K bits equal to one" constraint is automatically satisfied.
% This implementation was a quick and intuitive way to structure this
% function. It is by no means optimized and the end user is encouraged to
% experiment with other methods.

mutationChildren = zeros(length(parents),GenomeLength);
numVars = length(thisPopulation(1,:));

for i=1:length(parents)
    % Each parent produces a child.
    child = thisPopulation(parents(i),:);
    % The child is a random permutation of the parent's bits.
    mutationChildren(i,:) = child( randperm(numVars) );

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