遗传算法.zip_fourkcl_神经网络_神经网络方程_遗传算法

function [x,endPop,bPop,traceInfo] = ga(bounds,evalFN,evalOps,startPop,opts,... termFN,termOps,selectFN,selectOps,xOverFNs,xOverOps,mutFNs,mutOps) % 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 % endPop - the final population % 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 evaluation .m function % evalOps - options to pass to the evaluation function ([NULL]) % startPop - a matrix of solutions that can be initialized % 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]) % termFN - name of the .m termination function (['maxGenTerm']) % termOps - options string to be passed to the termination function % ([100]). % selectFN - name of the .m selection function (['normGeomSelect']) % selectOpts - options string to be passed to select after % select(pop,#,opts) ([0.08]) % xOverFNS - a string containing blank seperated names of Xover.m % files (['arithXover heuristicXover simpleXover']) % xOverOps - A matrix of options to pass to Xover.m files with the % 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 % files (['boundaryMutation multiNonUnifMutation ... % nonUnifMutation unifMutation']) % mutOps - A matrix of options to pass to Xover.m files with the % 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]) % Binary and Real-Valued Simulation Evolution for Matlab % Copyright (C) 1996 C.R. Houck, J.A. Joines, M.G. Kay % % C.R. Houck, J.Joines, and M.Kay. A genetic algorithm for function % optimization: A Matlab implementation. ACM Transactions on Mathmatical % Software, Submitted 1996. % % This program is free software; you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by % the Free Software Foundation; either version 1, or (at your option) % any later version. % % 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; if n<2 | n==6 | n==10 | n==12 disp('Insufficient arguements') end if n<3 %Default evalation opts. evalOps=[]; end if n<5 opts = [1e-6 1 0]; end if isempty(opts) opts = [1e-6 1 0]; end if any(evalFN<48) %Not using a .m file if opts(2)==1 %Float ga e1str=['x=c1; c1(xZomeLength)=', evalFN ';']; e2str=['x=c2; c2(xZomeLength)=', evalFN ';']; else %Binary ga e1str=['x=b2f(endPop(j,:),bounds,bits); endPop(j,xZomeLength)=',... evalFN ';']; end else %Are using a .m file if opts(2)==1 %Float ga e1str=['[c1 c1(xZomeLength)]=' evalFN '(c1,[gen evalOps]);']; e2str=['[c2 c2(xZomeLength)]=' evalFN '(c2,[gen evalOps]);']; else %Binary ga 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 termOps=[100]; termFN='maxGenTerm'; end if n<12 %Default muatation information if opts(2)==1 %Float GA mutFNs=['boundaryMutation multiNonUnifMutation nonUnifMutation unifMutation']; mutOps=[4 0 0;6 termOps(1) 3;4 termOps(1) 3;4 0 0]; else %Binary GA mutFNs=['binaryMutation']; mutOps=[0.05]; end end if n<10 %Default crossover information if opts(2)==1 %Float GA xOverFNs=['arithXover heuristicXover simpleXover']; xOverOps=[2 0;2 3;2 0]; else %Binary GA xOverFNs=['simpleXover']; xOverOps=[0.6]; end end if n<9 %Default select opts only i.e. roullete wheel. selectOps=[]; end if n<8 %Default select info selectFN=['normGeomSelect']; selectOps=[0.08]; end if n<6 %Default termination information termOps=[100]; termFN='maxGenTerm'; end if n<4 %No starting population passed given startPop=[]; end if isempty(startPop) %Generate a population at random %startPop=zeros(80,size(bounds,1)+1); startPop=initializega(80,bounds,evalFN,evalOps,opts(1:2)); end if opts(2)==0 %binary bits=calcbits(bounds,opts(1)); end xOverFNs=parse(xOverFNs); mutFNs=parse(mutFNs); xZomeLength = size(startPop,2); %Length of the xzome=numVars+fittness numVar = xZomeLength-1; %Number of variables popSize = size(startPop,1); %Number of individuals in the pop endPop = zeros(popSize,xZomeLength); %A secondary population matrix c1 = zeros(1,xZomeLength); %An individual c2 = zeros(1,xZomeLength); %An individual numXOvers = size(xOverFNs,1); %Number of Crossover operators numMuts = size(mutFNs,1); %Number of Mutation operators epsilon = opts(1); %Threshold for two fittness to differ oval = max(startPop(:,xZomeLength)); %Best value in start pop bFoundIn = 1; %Number of times best has changed done = 0; %Done with simulated evolution gen = 1; %Current Generation Number collectTrace = (nargout>3); %Should we collect info every gen floatGA = opts(2)==1; %Probabilistic application of ops display = opts(3); %Display progress while(~done) %Elitist Model [bval,bindx] = max(startPop(:,xZomeLength)); %Best of current pop best = startPop(bindx,:); if collectTrace traceInfo(gen,1)=gen; %current generation traceInfo(gen,2)=startPop(bindx,xZomeLength); %Best fittness traceInfo(gen,3)=mean(startPop(:,xZomeLength)); %Avg fittness traceInfo(gen,4)=std(startPop(:,xZomeLength)); end if ( (abs(bval - oval)>epsilon) | (gen==1)) %If we have a new best sol if display fprintf(1,'\n%d %f\n',gen,bval); %Update the display end if floatGA bPop(bFoundIn,:)=[gen startPop(bindx,:)]; %Update bPop Matrix else bPop(bFoundIn,:)=[gen b2f(startPop(bindx,1:numVar),bounds,bits)... startPop(bindx,xZomeLength)]; end bFoundIn=bFoundIn+1; %Update number of changes oval=bval; %Update the best val else if display fprintf(1,'%d ',gen); %Otherwise just update num gen end end endPop = feval(selectFN,startPop,[gen selectOps