gusucode.com > 遗传算法 gaot工具箱matlab源码程序 > code_rar/gaot/linerorderXover.m
function [c1,c2]=lox(p1,p2,bounds,genInfo,ops) % Linearorder crossover takes two parents P1,P2 and performs linear order % crossover for permutation strings. % % function [c1,c2] = linearOrderXover(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; c1=p1(1:sz);%zeros(1,sz); c2=p2(1:sz);%zeros(1,sz); cut1=round(rand*(sz-1)+1.5); cut2=round(rand*(sz-1)+1.5); while cut2 == cut1 cut2 = round(rand*sz + 0.5); end if cut1 > cut2 t = cut1; cut1 = cut2; cut2 = t; end for i=cut1:cut2 c1=strrep(c1,p2(i),-1); c2=strrep(c2,p1(i),-1); end g1=find(c1>-1); g2=find(c2>-1); c1=[c1(g1(1:(cut1-1))) p2(cut1:cut2) c1(g1(cut1:end)) p1(end)]; c2=[c2(g2(1:(cut1-1))) p1(cut1:cut2) c2(g2(cut1:end)) p2(end)];