遗传算法选取最优参数MATLAB程序.doc

复制代码在这里使用启发式算法GA（遗传算法）来进行参数寻优，用网格划分(grid search)来寻找最佳的参数c和g，虽然采用网格搜索能够找到在CV意义下的最高的分类准确率，即全局最优解，但有时候如果想在更大的范围内寻找最佳的参数c和g会很费时，采用启发式算法就可以不必遍历网格内的所有的参数点，也能找到全局最优解。关于遗传算法这里不打算过多介绍，想要学习的朋友可以自己查看相关资料。使用GA来进行参数寻优在在libsvm-mat-2.89-3FarutoUltimate3.0工具箱中已经实现gaSVMcgForClass.m（分类问题参数寻优）、gaSVMcgForRegress.m（回归问题参数寻优）。1. 利用GA参数寻优函数(分类问题):gaSVMcgForClass2. bestCVaccuracy,bestc,bestg,ga_option= 3. gaSVMcgForClass(train_label,train,ga_option)4. 输入：5. train_label:训练集的标签，格式要求与svmtrain相同。6. train:训练集，格式要求与svmtrain相同。7. ga_option:GA中的一些参数设置，可不输入，有默认值，详细请看代码的帮助说明。8. 输出：9. bestCVaccuracy:最终CV意义下的最佳分类准确率。10. bestc:最佳的参数c。11. bestg:最佳的参数g。12. ga_option:记录GA中的一些参数。13. =14. 利用GA参数寻优函数(回归问题):gaSVMcgForRegress15. bestCVmse,bestc,bestg,ga_option= 16. gaSVMcgForRegress(train_label,train,ga_option)17. 其输入输出与gaSVMcgForClass类似，这里不再赘述。复制代码gaSVMcgForClass.m源代码： 1. function BestCVaccuracy,Bestc,Bestg,ga_option = gaSVMcgForClass(train_label,train_data,ga_option)2. % gaSVMcgForClass3.4. %5. % by faruto6. %Email: QQ:516667408 /faruto BNU7. %last modified 2010.01.178.9. % 若转载请注明：10. % faruto and liyang , LIBSVM-farutoUltimateVersion 11. % a toolbox with implements for support vector machines based on libsvm, 2009. 12. % 13. % Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for14. % support vector machines, 2001. Software available at15. % .tw/cjlin/libsvm16.17. % 参数初始化18. if nargin = 219. ga_option = struct(maxgen,200,sizepop,20,ggap,0.9,.20. cbound,0,100,gbound,0,1000,v,5);21. end22. % maxgen:最大的进化代数,默认为200,一般取值范围为100,50023. % sizepop:种群最大数量,默认为20,一般取值范围为20,10024. % cbound = cmin,cmax,参数c的变化范围,默认为(0,10025. % gbound = gmin,gmax,参数g的变化范围,默认为0,100026. % v:SVM Cross Validation参数,默认为527.28. %29. MAXGEN = ga_option.maxgen;30. NIND = ga_option.sizepop;31. NVAR = 2;32. PRECI = 20;33. GGAP = ga_option.ggap;34. trace = zeros(MAXGEN,2);35.36. FieldID = .37. rep(PRECI,1,NVAR);ga_option.cbound(1),ga_option.gbound(1);ga_option.cbound(2),ga_option.gbound(2); .38. 1,1;0,0;0,1;1,1;39.40. Chrom = crtbp(NIND,NVAR*PRECI);41.42. gen = 1;43. v = ga_option.v;44. BestCVaccuracy = 0;45. Bestc = 0;46. Bestg = 0;47. %48. cg = bs2rv(Chrom,FieldID);49.50. for nind = 1:NIND51. cmd = -v ,num2str(v), -c ,num2str(cg(nind,1), -g ,num2str(cg(nind,2);52. ObjV(nind,1) = svmtrain(train_label,train_data,cmd);53. end54. BestCVaccuracy,I = max(ObjV);55. Bestc = cg(I,1);56. Bestg = cg(I,2);57.58. %59. while 1 60. % for gen = 1:MAXGEN61. FitnV = ranking(-ObjV);62. 63. SelCh = select(sus,Chrom,FitnV,GGAP);64. SelCh = recombin(xovsp,SelCh,0.7);65. SelCh = mut(SelCh);66. 67. cg = bs2rv(SelCh,FieldID);68. for nind = 1:size(SelCh,1)69. cmd = -v ,num2str(v), -c ,num2str(cg(nind,1), -g ,num2str(cg(nind,2);70. ObjVSel(nind,1) = svmtrain(train_label,train_data,cmd);71. end72. 73. Chrom,ObjV = reins(Chrom,SelCh,1,1,ObjV,ObjVSel);74. 75. if max(ObjV) BestCVaccuracy84. BestCVaccuracy = NewBestCVaccuracy;85. Bestc = cg_temp(I,1);86. Bestg = cg_temp(I,2);87. end88. 89. if abs( NewBestCVaccuracy-BestCVaccuracy ) = 10(-2) & .90. cg_temp(I,1) Bestc91. BestCVaccuracy = NewBestCVaccuracy;92. Bestc = cg_temp(I,1);93. Bestg = cg_temp(I,2);94. end 95. 96. trace(gen,1) = max(ObjV);97. trace(gen,2) = sum(ObjV)/length(ObjV);98. 99. gen = gen+1;100. 101. if gen =80 & .105. ( temp_NewBestCVaccuracy-BestCVaccuracy ) = 10(-2) 106. break;107. end108. if gen = MAXGEN109. break;110. end111. 112. end113. gen = gen -1;114. %115. figure;116. hold on;117. trace = round(trace*10000)/10000;118. plot(trace(1:gen,1),r*-,LineWidth,1.5);119. plot(trace(1:gen,2),o-,LineWidth,1.5);120. legend(最佳适应度,平均适应度,3);121. xlabel(进化代数,FontSize,12);122. ylabel(适应度,FontSize,12);123. axis(0 gen 0 100);124. grid on;125. axis auto;126.127. line1 = 适应度曲线AccuracyGAmethod;128. line2 = (终