gusucode.com > 遗传算法 gaot工具箱matlab源码程序 > code_rar/gaot/singleptXover.m
function [c1,c2]= singlePtX(p1,p2,bounds,Ops) % function [c1,c2] = singlePtXover(p1,p2,bounds,Ops) % p1 - the first parent ( [solution string function value] ) % p2 - the second parent ( [solution string function value] ) % bounds - the bounds matrix for the solution space % Ops - Options matrix for simple crossover [gen #SimpXovers]. % 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. sz=size(p1,2)-1; cut = round(rand*(sz-1)+0.5); %Generate random cut point U(1,n-1) pm1=p1(1:sz); pm2=p2(1:sz); c1=p1; c2=p2; c1(1:cut)=p1(1:cut); c2(1:cut)=p2(1:cut); for i=1:cut pm1=strrep(pm1,p2(i),-1); pm2=strrep(pm2,p1(i),-1); end c1((cut+1):sz)=p2(find(pm2>0)); c2((cut+1):sz)=p1(find(pm1>0));