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') endif n<3 %Default evalation opts.  evalOps=[];endif n<5  opts = [1e-6 1 0];endif isempty(opts)  opts = [1e-6 1 0];endif 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 ';'];  endelse %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];'];    endendif n<6 %Default termination information  termOps=[100];  termFN='maxGenTerm';endif 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];  endendif 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];  endendif n<9 %Default select opts only i.e. roullete wheel.  selectOps=[];endif n<8 %Default select info  selectFN=['normGeomSelect'];  selectOps=[0.08];endif n<6 %Default termination information  termOps=[100];  termFN='maxGenTerm';endif n<4 %No starting population passed given  startPop=[];endif isempty(startPop) %Generate a population at random  %startPop=zeros(80,size(bounds,1)+1);  startPop=initializega(80,bounds,evalFN,evalOps,opts(1:2));endif opts(2)==0 %binary  bits=calcbits(bounds,opts(1));endxOverFNs=parse(xOverFNs);mutFNs=parse(mutFNs);xZomeLength  = size(startPop,2); 	%Length of the xzome=numVars+fittnessnumVar       = xZomeLength-1; 		%Number of variablespopSize      = size(startPop,1); 	%Number of individuals in the popendPop       = zeros(popSize,xZomeLength); %A secondary population matrixc1           = zeros(1,xZomeLength); 	%An individualc2           = zeros(1,xZomeLength); 	%An individualnumXOvers    = size(xOverFNs,1); 	%Number of Crossover operatorsnumMuts      = size(mutFNs,1); 		%Number of Mutation operatorsepsilon      = opts(1);                 %Threshold for two fittness to differoval         = max(startPop(:,xZomeLength)); %Best value in start popbFoundIn     = 1; 			%Number of times best has changeddone         = 0;                       %Done with simulated evolutiongen          = 1; 			%Current Generation NumbercollectTrace = (nargout>3); 		%Should we collect info every genfloatGA      = opts(2)==1;              %Probabilistic application of opsdisplay      = 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]); %Select    if floatGA %Running with the model where the parameters are numbers of ops    for i=1:numXOvers,      for j=1:xOverOps(i,1),	      a = round(rand*(popSize-1)+1); 	%Pick a parent	      b = round(rand*(popSize-1)+1); 	%Pick another parent	      xN=deblank(xOverFNs(i,:)); 	%Get the name of crossover function	      [c1 c2] = feval(xN,endPop(a,:),endPop(b,:),bounds,[gen xOverOps(i,:)]);		     if c1(1:numVar)==endPop(a,(1:numVar)) %Make sure we created a new 	        c1(xZomeLength)=endPop(a,xZomeLength); %solution before evaluating	     elseif c1(1:numVar)==endPop(b,(1:numVar))	            c1(xZomeLength)=endPop(b,xZomeLength);	         else 	          %[c1(xZomeLength) c1] = feval(evalFN,c1,[gen evalOps]);	            eval(e1str);	        end	     if c2(1:numVar)==endPop(a,(1:numVar))	        c2(xZomeLength)=endPop(a,xZomeLength);	     elseif c2(1:numVar)==endPop(b,(1:numVar))	            c2(xZomeLength)=endPop(b,xZomeLength);	         else 	         %[c2(xZomeLength) c2] = feval(evalFN,c2,[gen evalOps]);	            eval(e2str);	         end      		     endPop(a,:)=c1;	     endPop(b,:)=c2;        end     end       for i=1:numMuts,        for j=1:mutOps(i,1),	        a = round(rand*(popSize-1)+1);	        c1 = feval(deblank(mutFNs(i,:)),endPop(a,:),bounds,[gen mutOps(i,:)]);	        if c1(1:numVar)==endPop(a,(1:numVar)) 	           c1(xZomeLength)=endPop(a,xZomeLength);	        else	        %[c1(xZomeLength) c1] = feval(evalFN,c1,[gen evalOps]);	           eval(e1str);	        end	        endPop(a,:)=c1;        end    end      else %We are running a probabilistic model of genetic operators    for i=1:numXOvers,       xN=deblank(xOverFNs(i,:)); 	%Get the name of crossover function       cp=find(rand(popSize,1)<xOverOps(i,1)==1);       if rem(size(cp,1),2)            cp=cp(1:(size(cp,1)-1));        end       cp=reshape(cp,size(cp,1)/2,2);       for j=1:size(cp,1)	      a=cp(j,1);          b=cp(j,2); 	      [endPop(a,:) endPop(b,:)] = feval(xN,endPop(a,:),endPop(b,:),...	                                        bounds,[gen xOverOps(i,:)]);      end   end   for i=1:numMuts      mN=deblank(mutFNs(i,:));      for j=1:popSize	     endPop(j,:) = feval(mN,endPop(j,:),bounds,[gen mutOps(i,:)]);	     eval(e1str);      end  end  end    gen=gen+1;  done=feval(termFN,[gen termOps],bPop,endPop); %See if the ga is done  startPop=endPop; 			%Swap the populations    [bval,bindx] = min(startPop(:,xZomeLength)); %Keep the best solution  startPop(bindx,:) = best; 		%replace it with the worstend[bval,bindx] = max(startPop(:,xZomeLength));if display   fprintf(1,'\n%d %f\n',gen,bval);	  endx=startPop(bindx,:);if opts(2)==0 %binary  x=b2f(x,bounds,bits);  bPop(bFoundIn,:)=[gen b2f(startPop(bindx,1:numVar),bounds,bits)...      startPop(bindx,xZomeLength)];else  bPop(bFoundIn,:)=[gen startPop(bindx,:)];endif collectTrace  traceInfo(gen,1)=gen; 		%current generation  traceInfo(gen,2)=startPop(bindx,xZomeLength); %Best fittness  traceInfo(gen,3)=mean(startPop(:,xZomeLength)); %Avg fittnessend