ga.rar_GA-SVM

function [x,endPop,bPop,traceInfo] = ga(bounds,evalFN,evalOps,startPop,opts,... termFN,termOps,selectFN,selectOps,xOverFNs,xOverOps,mutFNs,mutOps) % goat工具箱是网上流传最广的一个遗传工具箱，以下针对其主函数的注释。 %GA run a genetic algorithm % function [x,endPop,bPop,traceInfo]=ga(bounds,evalFN,evalOps,startPop,opts, % termFN,termOps,selectFN,selectOps, % xOverFNs,xOverOps,mutFNs,mutOps) % % Output Arguments: % x - the best solution found during the course of the run 求得的最优解 % endPop - the final population 最终得到的种群 % bPop - a trace of the best population 最优种群的一个搜索轨迹 % traceInfo - a matrix of best and means of the ga for each generation 每一代种群的最优个体和均值 % % Input Arguments: % bounds - a matrix of upper and lower bounds on the variables 代表变量上下界的矩阵 % evalFN - the name of the evaluation .m function 适应度函数所在的m文件 % evalOps - options to pass to the evaluation function ([NULL]) % 传递给适应度函数的参数,默认为空 % startPop - a matrix of solutions that can be initialized 初始种群 % from initialize.m % opts - [epsilon prob_ops display] change required to consider two % solutions different, prob_ops 0 if you want to apply the % genetic operators probabilisticly to each solution, 1 if % you are supplying a deterministic number of operator % applications and display is 1 to output progress 0 for % quiet. ([1e-6 1 0]) % options--选择编码形式(浮点编码或是二进制编码)[epsilon prob_ops display],如 %prob_ops-变量进行二进制编码时指定的精度 %prob_ops--为1时选择浮点编码，否则为二进制编码,由precision指定精度) %display0或1控制是否输出结果；默认为[1e-6 1 0] % termFN - name of the .m termination function (['maxGenTerm']) % 终止函数所在的m文件，maxGenTerm为求最大值时的终止函数 % termOps - options string to be passed to the termination function 传递个终止函数的参数，一般为需要遗传的代数 % ([100]).maxGenTerm将在这里面取得最大值 % selectFN - name of the .m selection function (['normGeomSelect']) 选择函数的名称 % selectOpts - options string to be passed to select after 传递给选择函数的参数，一般为变异概率 % select(pop,#,opts) ([0.08]) % xOverFNS - a string containing blank seperated names of Xover.m 交叉函数名称表，以空格分开 % files (['arithXover maxGenTermXover simpleXover']) % xOverOps - A matrix of options to pass to Xover.m files with the 传递给交叉函数的参数表 % first column being the number of that xOver to perform % similiarly for mutation ([2 0;2 3;2 0]) % mutFNs - a string containing blank seperated names of mutation.m 变异函数表% files (['boundaryMutation multiNonUnifMutation ... % nonUnifMutation unifMutation']) % mutOps - A matrix of options to pass to Xover.m files with the 传递给变异函数的参数表 % first column being the number of that xOver to perform % similiarly for mutation ([4 0 0;6 100 3;4 100 3;4 0 0]) %怎样建立适应度函数？ % 例1： function [x, val] = gaDemo1Eval(sol,options) % x=sol(1); % val = x + 10*sin(5*x)+7*cos(4*x); % Sol 参量X(种群个体)。 % %选择 options是 [当前代值evalOps ] %函数必须反回两个值, val 和串sol 。这做以便 您的评估的修理或改进。 %例二：f(x1,x2)=-20*exp(-0.2*sqrt(0.5*(x1.^2+x2.^2)))-exp(0.5*(cos(2*pi*x1)+cos(2*pi*x2)))+22.71282的最小值 %先建原函数文件 % function [eval]=f(sol) % numv=size(sol,2); % x=sol(1:numv); % eval=-20*exp(-0.2*sqrt(sum(x.^2)/numv)))-exp(sum(cos(2*pi*x))/numv)+22.71282; %再建 适应度函数的matlab代码文件 % function [sol,eval]=fitness(sol,options) % numv=size(sol,2)-1; % x=sol(1:numv); % eval=f(x); % eval=-eval; %遗传算法的matlab代码 %bounds=ones(2,1)*[-5 5]; %[p,endPop,bestSols,trace]=ga(bounds,'fitness') %画图查看 % fplot('f',[0 9])； %plot (endPop(:,1),endPop(:,2),'ro') % %如果没有给定初始种群，则随机生成 %startPop=zeros(80,size(bounds,1)+1);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. %%$Log: ga.m,v $ %Revision 1.10 1996/02/02 15:03:00 jjoine % Fixed the ordering of imput arguments in the comments to match % the actual order in the ga function. % %Revision 1.9 1995/08/28 20:01:07 chouck % Updated initialization parameters, updated mutation parameters to reflect % b being the third option to the nonuniform mutations % %Revision 1.8 1995/08/10 12:59:49 jjoine %Started Logfile to keep track of revisions % n=nargin;%nargin得到函数的参数个数 if n<2 | n==6 | n==10 | n==12 disp('Insufficient arguements') end if n<3 %Default evalation opts. evalOps=[];%适应度函数的参数为空 % 其它默认值 为迭代100次 传递给选择函数的参数，一般为变异概率0.05 % 交叉算子(['arithXover heuristicXover simpleXover'])参数为([2 0;2 3;2 0]) % 变异算子(['boundaryMutation multiNonUnifMutation nonUnifMutation unifMutation']]) % 参数为[4 0 0;6 100 3;4 100 3;4 0 0] end if n<5 opts = [1e-6 1 0];%进行二进制编码，并指定精度 end if isempty(opts) opts = [1e-6 1 0];%默认情况下为二进制编码 end if any(evalFN<48) %Not using a .m file if opts(2)==1 %Float ga,opts函数的第二个参数为1，则使用浮点数编码 e1str=['x=c1; c1(xZomeLength)=', evalFN ';']; e2str=['x=c2; c2(xZomeLength)=', evalFN ';']; else %Binary ga，opts函数的第二个参数为0，则使用二进制编码 e1str=['x=b2f(endPop(j,:),bounds,bits); endPop(j,xZomeLength)=',... evalFN ';']; end else %Are using a .m file if opts(2)==1 %Float ga，opts函数的第二个参数为1，则使用浮点数编码 c1：个体 ； c1(xZomeLength)：个体适应度 e1str=['[c1 c1(xZomeLength)]=' evalFN '(c1,[gen evalOps]);']; e2str=['[c2 c2(xZomeLength)]=' evalFN '(c2,[gen evalOps]);']; else %Binary ga，opts函数的第二个参数为0，则使用二进制编码 e1str=['x=b2f(endPop(j,:),bounds,bits);[x v]=' evalFN ... '(x,[gen evalOps]); endPop(j,:)=[f2b(x,bounds,bits) v];']; end end if n<6 %Default termination information， 默认终止函数信息 termOps=[100];%默认遗传100代 termFN='maxGenTerm'; end if n<12 %Default muatation information，默认变异信息 if opts(2)==1 %Float GA，浮点数编码 mutFNs=['boundaryMutation multiNonUnifMutation nonUnifMutation unifMutation']; mutOps=[4 0 0;6 termOps(1) 3;4 termOps(1) 3;4 0 0]; else %Binary GA，二进制编码 mutFNs=['binaryMutation'];%采用二进制变异 mutOps=[0.05];%默认变异率为0.05 end end if n<10 %Default crossover information，默认交叉信息 if opts(2)==1 %Float GA，浮点数编码 xOverFNs=['arithXover heuristicXover simpleXover']; xOverOps=[2 0;2 3;2 0]; else %Binary GA， 二进制编码 xOverFNs=['simpleXover']; xOverOps=[0.6]; end end if n<9 %Default select opts only i.e. roullete wheel.默认为轮盘赌选择算法 selectOps=[]; end if n<8 %Default select info selectFN=['normGeomSelect']; selectOps=[0.08]; end if n<6 %Default termination information，默认终止函数信息 termOps=[100];%默认遗传100代 termFN='maxGenTerm'; end if n<4 %No starting population passed given，没有给定初始化的种群 startPop=[]; en