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);endc1((cut+1):sz)=p2(find(pm2>0));c2((cut+1):sz)=p1(find(pm1>0));