gusucode.com > 遗传算法 gaot工具箱matlab源码程序 > code_rar/gaot/uniformXover.m
function [ch1,ch2,t] = uniformxover(par1,par2,bounds,Ops) % Uniform crossover takes two parents P1,P2 and performs uniform % crossover on a permuation string. % % 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(par1,2)-1; ch1 = par1; ch2 = par2; t = round(rand(1,sz)); szt = sum(t); idx = 1:sz; idxt = idx(logical(t)); plst = idxt; for i = 1:szt plst(i) = find(par2 == par1(idxt(i))); end plst = sort(plst); for i = 1:szt ch1(idxt(i)) = par2(plst(i)); end szt = sz - szt; idxt = idx(~t); plst = idxt; for i = 1:szt plst(i) = find(par1 == par2(idxt(i))); end plst = sort(plst); for i = 1:szt ch2(idxt(i)) = par1(plst(i)); end