gusucode.com > 遗传算法 gaot工具箱matlab源码程序 > code_rar/gaot/heuristicXover.m
function [c1,c2] = heuristicXover(p1,p2,bounds,Ops) % Heuristic crossover takes two parents P1,P2 and performs an extrapolation % along the line formed by the two parents outward in the direction of the % better parent. % % function [c1,c2] = heuristicXover(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 for heuristic crossover, [gen #heurXovers number_of_retries] % 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. retry=Ops(3); % Number of retries i=0; good=0; b1=bounds(:,1)'; b2=bounds(:,2)'; numVar = size(p1,2)-1; % Determine the best and worst parent if(p1(numVar+1) > p2(numVar+1)) bt = p1; wt = p2; else bt = p2; wt = p1; end while i<retry % Pick a random mix amount a = rand; % Create the child c1 = a * (bt - wt) + bt; % Check to see if child is within bounds if (c1(1:numVar) <= b2 & (c1(1:numVar) >= b1)) i = retry; good=1; else i = i + 1; end end % If new child is not feasible just return the new children if(~good) c1 = wt; end % Crossover functions return two children therefore return the best % and the new child created c2 = bt;