利用GA優化SVM參數的一點小探索利用GA優化SVM參數的一點小探索其中GA代碼思路，部分參照版主shi01fg的進行的修改.部分參照雷英杰《MATLAB遺傳算法工具箱與應用》中算法自己實現.O（E_E）O.利用GA優化SVM中的c和gPS:圖像好像有問題，怎么最佳適應度有的點會比平均的小？怪?不管了.反正結果圖如此，晚上回來再看看怎么回事..先這樣..O（E_E）OPPS:那個啥bug找到了..已作了修改...~~還做了一點小優化從使用gridsearch優化SVM參數到用TSO,再到用GA優化SVM參數,俺總結出一個經驗,把C控制住了.不能讓C太大!！!無論你用什么辦法優化，在分類準確率都達到最高的參數里,優先選擇C小的那組參數才是上策..當然只是我個人的一點小經驗!!O（n_n）O

Matlablfc壇iLoveMMatlablfc壇iLoveMQ i = i ■ i I L_oio20aa40 5。eozo進化代數 最佳適應度——平均適應度圖:結果：復制內容到剪貼板代碼：bestc=7.7161bestg=2.7382bestCVaccuarcy=98.8764Accuracy=100%(89/89)(classification)Accuracy=96.6292%(86/89)(classification)Elapsedtimeis41.767334seconds.代碼：測試文件代碼：復制內容到剪貼板代碼：%%清空環境clcclear%%loadwine;train=[wine(1:30,:);wine(60:95,:);wine(131:153,:)];train_label=[wine_labels(1:30);wine_labels(60:95);wine_labels(131:153)];test=[wine(31:59,:);wine(96:130,:);wine(154:178,:)];test_label=[wine_labels(31:59);wine_labels(96:130);wine_labels(154:178)];[train,pstrain]=mapminmax(train');pstrain.ymin=0;pstrain.ymax=1;[train,pstrain]=mapminmax(train,pstrain);[test,pstest]=mapminmax(test');pstest.ymin=0;pstest.ymax=1;[test,pstest]=mapminmax(test,pstest);train=train';test=test';%%tic;ga_option.maxgen=200;ga_option.sizepop=50;ga_option.pCrossover=0.4;ga_option.pMutation=0.01;ga_option.cbound=[0.1,100];ga_option.gbound=[0.01,1000];ga_option.v=3;[bestCVaccuarcy,bestc,bestg,ga_option]=gaSVMcgForClass(train_label,train,ga_option);bestcbestgbestCVaccuarcycmd=['-c',num2str(bestc),'-g',num2str(bestg)];model=svmtrain(train_label,train,cmd);[pretrain,trainacc]=svmpredict(train_label,train,model);[pretest,testacc]=svmpredict(test_label,test,model);toc;gaSVMcgForClass.m代碼復制內容到剪貼板代碼：function[bestCVaccuarcy,bestc,bestg,ga_option]=gaSVMcgForClass(train_label,train,ga_option)%gaSVMcgForClass%byfaruto%2009.10.07%%參數初始化ifnargin==2ga_option=struct('maxgen',100,'sizepop',20,'pCrossover',0.4,'pMutation',0.01,...'cbound',[0.1,100],'gbound',[0.01,1000],'v',3);end%maxgen:最大的進化代數,默認為100,一般取值范圍為[100,500]%sizepop:種群最大數量，默認為20,一般取值范圍為[20,100]%pCrossover:交叉概率,默認為0.4,一般取值范圍為[0.4,0.99]%pMutation:變異概率,默認為0.01，一般取值范圍為[0.0001,0.1]%cbound=[cmin,cmax],參數c的變化范圍，默認為[0.1,100]%gbound=[gmin,gmax],參數g的變化范圍，默認為[0.01,1000]%v:SVMCrossValidation參數，默認為3c_len_chromosome=ceil(log2((ga_option.cbound(2)-ga_option.cbound(1))*100));g_len_chromosome=ceil(log2((ga_option.gbound(2)-ga_option.gbound(1))*100));len_chromosome=c_len_chromosome+g_len_chromosome;%將種群信息定義為一個結構體individuals=struct('fitness',zeros(1,ga_option.sizepop),...'chromosome',zeros(ga_option.sizepop,len_chromosome));%每一代種群的平均適應度avgfitness_gen=zeros(1,ga_option.maxgen);%每一代種群的最佳適應度bestfitness_gen=zeros(1,ga_option.maxgen);%最佳適應度bestfitness=0;%適應度最好的染色體bestchromosome=zeros(1,len_chromosome);%%初始化種群fori=1:ga_option.sizepop%編碼individuals.chromosome(i,:)=unidrnd(2,1,len_chromosome)-1;%解碼[c,g]=ga_decode(individuals.chromosome(i,:),ga_option.cbound,ga_option.gbound);%計算初始適應度(CV準確率)cmd=['-v',num2str(ga_option.v),'-c',num2str(c),'-g',num2str(g)];individuals.fitness(i)=svmtrain(train_label,train,cmd);end%找最佳的適應度和最好的染色體的位置[bestfitness,bestindex]=max(individuals.fitness);%最好的染色體bestchromosome=individuals.chromosome(bestindex,:);%初始染色體的平均適應度avgfitness_gen(1)=sum(individuals.fitness)/ga_option.sizepop;%%迭代尋優fori=1:ga_option.maxgen%SelectionOperatorindividuals=Selection(individuals,ga_option);%CrossoverOperatorindividuals=Crossover(individuals,ga_option);%MutationOperatorindividuals=Mutation(individuals,ga_option);%計算適應度forj=1:ga_option.sizepop%解碼[c,g]=ga_decode(individuals.chromosome(j,:),ga_option.cbound,ga_option.gbound);%計算初始適應度(CV準確率)cmd=['-v',num2str(ga_option.v),'-c',num2str(c),'-g',num2str(g)];individuals.fitness(j)=svmtrain(train_label,train,cmd);end%找最佳的適應度和最好的染色體的位置[new_bestfitness,bestindex]=max(individuals.fitness);%最好的染色體new_bestchromosome=individuals.chromosome(bestindex,:);[new_c,g]=ga_decode(new_bestchromosome,ga_option.cbound,ga_option.gbound);[c,g]=ga_decode(bestchromosome,ga_option.cbound,ga_option.gbound);ifnew_bestfitness==bestfitness&&new_c<cbestfitness=new_bestfitness;bestchromosome=new_bestchromosome;endifnew_bestfitness>bestfitnessbestfitness=new_bestfitness;bestchromosome=new_bestchromosome;end%這一代染色體的最佳適應度bestfitness_gen(i)=bestfitness;%這一代染色體的平均適應度avgfitness_gen(i)=sum(individuals.fitness)/ga_option.sizepop;end%%結果分析figure;holdon;plot(bestfitness_gen,'r');plot(avgfitness_gen);legend('最佳適應度'，’平均適應度')；title(['適應度曲線'，'(終止代數二'，num2str(ga_option.maxgen),',種群數量pop='，num2str(ga_option.sizepop),'