gusucode.com > 遗传算法 gaot工具箱matlab源码程序 > code_rar/gaot/partmapXover.m
function [ch1,ch2] = partmapXover(par1,par2,bounds,Ops) % Partmap crossover takes two parents P1,P2 and performs a partially % mapped crossover. % % function [c1,c2] = partmapXover(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; pos1 = round(rand*sz + 0.5); pos2 = round(rand*sz + 0.5); while pos2 == pos1 pos2 = round(rand*sz + 0.5); end if pos1 > pos2 t = pos1; pos1 = pos2; pos2 = t; end ss1 = par1(pos1:pos2); ss2 = par2(pos1:pos2); ch1 = par2; ch2 = par1; for i = [1:pos1-1 pos2+1:sz] ch1(i) = par1(i); j = find(ch1(i) == ss2); while ~isempty(j) ch1(i) = ss1(j); j = find(ch1(i) == ss2); end ch2(i) = par2(i); j = find(ch2(i) == ss1); while ~isempty(j) ch2(i) = ss2(j); j = find(ch2(i) == ss1); end end