ga_weights.m

遗传算法matlab实现

function weights = ga_weights(numSVM,MODELS,Xdev,labels)

Ntest = size(Xdev,1);


group=zeros(100,numSVM);% 100 for the size of group. 400 for the coding length
fit=zeros(1,100)+0.2;
for i=1:100
    group(i,:)=round(rand(1,numSVM));
end



N=200;% iterations
grp=zeros(N,numSVM); % save the best invidual of each iteration


fit_optimal=zeros(1,N);
generation=0;

while generation<N
    
    generation=generation+1;
%Search for the optimal individual.
     [best,index]=max(fit);
%Compute the selection probability with the fitness of each individual.
     p_s=fit/sum(fit);
%select mating pool
     parent=select_parent(p_s);
%For the selected mating pool apply the crossover operation,temperate group is n_group
     n_group=crossover(group,parent,0.8,numSVM);
%For the temperate group, apply the mutation operation, N-1 new group is n_group.
     n_group=mutation(n_group,0.005,numSVM);

         
%Put maximize individual into new generation, and form the fininal new generation group.
     n_group(100,:)=group(index,:);
     group=n_group;
     fit=zeros(1,100);
     p_s=zeros(1,100);
     for g=1:100 % use the truenum on test set as fitness value
         
         Woutput=zeros(Ntest,1);
         
             
         for k=1:numSVM %for each svm
                 
             Woutput = Woutput + group(g,k)*simlssvm(MODELS{k},Xdev);
                 
         end
         
         for i=1:Ntest
             
             if Woutput(i)*labels(i) > 0
                 fit(g) = fit(g)+1;
             end
             
         end

         
     
     end
     
     fit = fit/Ntest;
     
     [best,index]=max(fit);
     iteration=generation   

     grp(generation,:)=n_group(index,:);
     result=[best,index]   
     fit_optimal(generation)=best;
     
     
 end

save grp grp;