www.gusucode.com > 遗传算法 gaot工具箱matlab源码程序 > code_rar/gaot/orderbasedXover.m
function [c1,c2]= oox(p1,p2,bounds,Ops) % Orderbased crossover takes two parents P1,P2 and performs order % based crossover by Davis. % % function [c1,c2] = orderbasedXover(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; n=floor(sz/2); cut1 = round(rand*(n-1)+0.5); %Generate random cut point U(1,n/2); cut2 = round(rand*(sz-cut1-1)+1+cut1); %Generate random cut point U(cut1+1,n-1); pm1=p1(1:end-1); pm2=p2(1:end-1); c1=p1; c2=p2; for i=[1:cut1 (cut2+1):sz] pm1=strrep(pm1,p2(i),-1); pm2=strrep(pm2,p1(i),-1); end c1((cut1+1):cut2)=p2(find(pm2>0)); c2((cut1+1):cut2)=p1(find(pm1>0));