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上传时间: 2015-06-12
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程序完整 function [x,traceInfo]=HGA(bounds,evalFN,FN1,FN2,pc,pm,ps,la,mref,maxGen) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Output Arguments: % x - the best solution found during the course of the run % bPop - a trace of the best population % traceInfo - a matrix of best and means of the ga for each generation % % Input Arguments: % bounds - a matrix of upper and lower bounds on the variables % evalFN - the name of the evaluat ion .m function % FN1 - 为数据目标函数; % FN2 - 为模型目标函数 % la -a initial factot of regularization; % mref -a reference model % pc -Probability of cross % pm -Probability of mutation % maxGen -Maximum Number of Generations %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% M=40; %定义种群规模 startPop =initialize(M,bounds); xZomeLength =size(startPop,2)+1; %返回个体的变量数目加上适值所占一位 numVar =xZomeLength-1; %变量数目 popSize =size(startPop, 1); %得到个体大小 endPop =zeros(popSize,xZomeLength); %最终得到的种群 c1 =zeros(1,xZomeLength); %得到一个个体，为交叉准备 c2 =zeros(1,xZomeLength); %得到另一个个体，为交叉准备 gen =1; %Current Generation Number，当前的种群数 xOverFNs =['arithXover heuristicXover simpleXover']; %交叉函数名称表，以空格分开 mutFNs =['multiNonUnifMutation nonUnifiMutation unifMutation']; %变异函数表 xOverFNs =parse(xOverFNs); %分析交叉函数 mutFNs =parse(mutFNs); numXOvers =size(xOverFNs, 1 ); numMuts =size(mutFNs, 1); while(gen<=maxGen) for i=1:popSize startPop(i,xZomeLength)=feval(evalFN,startPop(i,1:numVar),la,mref); end [bval,bindx]=max(startPop(:,xZomeLength)); best=startPop(bindx,:); traceInfo(gen,1)=gen; %每一代种群的最优个体和代数 traceInfo(gen,2)=startPop(bindx,xZomeLength); %找出适应值最优个体 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %选择操作 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% endPop =select(startPop); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %交叉操作 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ct=0; a=rand(popSize, 1); for i=1:popSize if(a(i, 1 )bounds(i,2) c1(i)=bounds(i,2); end end c1(xZomeLength)=feval(evalFN,c1(1:numVar),la,mref); endPop(a,:)=c1; stt=stt+1; end gen=gen+1; startPop=endPop; [bval,bindx]=min(startPop(:,xZomeLength)); startPop(bindx,:)=best; fl=FN1(best(1:numVar)); %最优个体保存策略 f2=FN2(best(1:numVar),mref); %更新止则化囚子 la=fl/(fl+f2); end [bval,bindx]=max(startPop(:,xZomeLength)); traceInfo(gen,1)=gen; traceInfo(gen,2)=startPop(bindx,xZomeLength); %最优个体的适应值 x=startPop(bindx,:); %遗传优化的最优解 ...展开收缩
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