www.pudn.com > matlab.rar > normGeomSelect.m, change:1996-02-06,size:2311b

function[newPop] = normGeomSelect(oldPop,options) % NormGeomSelect is a ranking selection function based on the normalized % geometric distribution. % % function[newPop] = normGeomSelect(oldPop,options) % newPop - the new population selected from the oldPop % oldPop - the current population % options - options to normGeomSelect [gen probability_of_selecting_best] % 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. q=options(2); % Probability of selecting the best e = size(oldPop,2); % Length of xZome, i.e. numvars+fit n = size(oldPop,1); % Number of individuals in pop newPop = zeros(n,e); % Allocate space for return pop fit = zeros(n,1); % Allocates space for prob of select x=zeros(n,2); % Sorted list of rank and id x(:,1) =[n:-1:1]'; % To know what element it was [y x(:,2)] = sort(oldPop(:,e)); % Get the index after a sort r = q/(1-(1-q)^n); % Normalize the distribution, q prime fit(x(:,2))=r*(1-q).^(x(:,1)-1); % Generates Prob of selection fit = cumsum(fit); % Calculate the cumulative prob. func rNums=sort(rand(n,1)); % Generate n sorted random numbers fitIn=1; newIn=1; % Initialize loop control while newIn<=n % Get n new individuals if(rNums(newIn)<fit(fitIn)) newPop(newIn,:) = oldPop(fitIn,:); % Select the fitIn individual newIn = newIn+1; % Looking for next new individual else fitIn = fitIn + 1; % Looking at next potential selection end end end