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);endif cut1 > cut2   t = cut1; cut1 = cut2; cut2 = t;endfor i=cut1:cut2  c1=strrep(c1,p2(i),-1);  c2=strrep(c2,p1(i),-1);endg1=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)];