function [c1,c2] = BSinglePointXover(p1,p2,bounds,Ops)
// Simple crossover takes two parents P1,P2 and performs simple single point
// crossover.  
//
// function [c1,c2] = simpleXover(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.

numVar = size(p1,2)-1; 			// Get the number of variables 
// Pick a cut point randomly from 1-number of vars
cPoint = round(rand(1) * (numVar-2)) + 1;

c1 = [p1(1:cPoint) p2(cPoint+1:numVar+1)]; // Create the children
c2 = [p2(1:cPoint) p1(cPoint+1:numVar+1)];