flexga.m

一个遗传算法工具包

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% flexga	               			flexga	      					flexga
% =========================================================================  
% =========================================================================  
% Genetic Algorithms Tools for MathTools 
% =========================================================================  
%                             Copyright (c) 1999, 2000
%                       Flexible Intelligence Group (FIG)       	             
%                   P. O. Box 861477, Tuscaloosa, AL 35486, USA.            
% =========================================================================  
%  PURPOSE   
%         Command line execution of GA module
% .........................................................................   
% GIVEN
%
% fname	=	A  fname.m file that contains the definition of the performance measure
%			The format should be PI=fname(P), 
%			where 
%				PI= the scalar value of performance
%				P = vector of parameters.
%
% P_type(1:Noofp)	=	A vector that describes the type of parameter used. 
%			The options are:
%				1=Integer type
%				2=Real type that takes discrete values
%				> 2 Real type that takes continuous values (this number also defines 
%					the number of bits that are used to represent each parameter 
%					using stochastic GA).
%
% P_min(1:Noofp)	=	A vector of minimum values for the parameter vector.
%			If  P_type (i) <= 2, P_min(i) represents the minimum value for P(i).
%			If  P_type (i) > 2, P_min(i)  represents an approximate value for the 
%				minimum of P(i). 
%
% P_max(1:Noofp)	=	A vector of maximum values for the parameter vector.
%			If  P_type (i) <= 2, P_max(i) represents the maximum value for P(i).
%			If  P_type(i) > 2, P_max(i) represents an approximate value for the
%				maximum of P(i).
%
% P_res(1:Noofp)	= 	A vector of resolution values for the parameter vector.
%			If  P_type (i) <= 2, P_res(i) represents the resolution value for P(i).
%			If  P_type(i) > 2, P_res(i) is ignored. 
%
% gap(1)	= 	Type of GA
%				1= Regular GA
%				2= Micro-GA
%				3=Steady State GA 
% gap(2)	=	# of generations for evolution
% gap(3)	=	population size
% gap(4)	=	No of peaks;     
% gap(5)	=	Type of selection desired
%				1= Roulette Wheel Selection
%				2= Tournament Selection
%				3= Ranking Selection
% gap(6)	=	Tournament size (> 1 if Tournament selection is chosen).
% gap(7)	=	Steady State population size (>1 if Steady State GA is chosen)
% gap(8)	=	Probability of Crossover (0 - 1)   
% gap(9)	= 	Number of crossover points ( > 1)
% gap(10)	=	Probability of Mutation (0 - 1)        
% gap(11)	= 	Probability of Gaussian Mutation (0 - 1)	[OPTIONAL]
% gap(12)	=	Micro-GA inner loop # of Generations   	[OPTIONAL]	
% gap(13)	=	# of asexual children for stochastic coding	[OPTIONAL]
% 
% G_disp	=	Gap Display options			[OPTIONAL]
%		=	0 (show no plots, no display)
%		= 	1 (Default, show plots of evolution)
%		= 	2 (show only Generation count)
%		=	3 (show data on screen and no plots. Data includes Generation #, 
%			    Max, Min, Avg Values)
% ...........................................................................   
%  RETURNS
% Jmax(1:Ngen)			=	A vector of maximum performance index values 
% Jmin(1:Ngen)			=	A vector of minimum performance index values
% Javg(1:Ngen)	 		=	A vector of average performance index values
% bestP(1:Noofpeaks,1:Noofp)	=	A matrix of best Parameter sets
% PI(1:popsize)			=	A vector of  fitness functions for the last generation   
%
% where
%	Ngen			= 	# of generations
%	Noofp			= 	# of parameters
%	Noofpeaks		= 	# of desired sub-optimal solutions
%	popsize			= 	population Size
%
%.............................................................................
%   USES  
%       fmcclust, fminit.m, fminipop.m, fmdecode.m, fmnich.m, fmstats.m, fmgen.m,
%       fmscreen, 
%       rand, if, load, figure, hold, for, feval, while, sort,   
%       subplot, plot, xlabel, ylabel, axis, title, pause, save  
% =========================================================================   
%  SYNTAX  /  USAGE   
%  [Jmax,Jmin,Javg,opt_P,PI]=flexga('JPERF',P_min,P_max,P_res,P_type,gap,G_disp)
%  ========================================================================   
%  CODE    
function [max_PI,min_PI,avg_PI,bestP,PI]=flexga(fname,P_min,P_max,P_res,P_type,gap,G_disp)

if size(P_min,1) ~= size(P_max,1)
   disp(['Size mismatch in P_min and P_max vectors.']);
   return;   
end

if size(P_min,1) ~= size(P_res,1)
   disp(['Size mismatch in P_min and P_res vectors.']);
   return;   
end

if size(P_min,1) ~= size(P_type,1)
   disp(['Size mismatch in P_min and P_res vectors.']);
   return;  
end

if (P_max < P_min ~= 0)
   disp(['P_max is not greater than P_min.']);
   return;   
end

 %------------------------------------------------  
 % Initialize ga_parameters  (call fminit)
 %------------------------------------------------  
 [gap,fpd,fud,mean_table,sigma_table,pot_table]=fminit(P_min,P_max,P_res,P_type,gap);

 if nargin < 7, G_disp=1; end

 if (2*round(gap(3)/2)-gap(3)) == 0, gap(3)=gap(3)+1; end 

 Nga_choice	= gap(1);
 if Nga_choice==3, gap(7)=fix(gap(3)/2); end

 Ngen		= gap(2);
 Npop		= gap(3);
 Noofpeaks	= gap(4);
 Nselect		= gap(5);
 Tsize		= gap(6);
 Nsspop		= gap(7);
 pcross		= gap(8); 
 Ncross		= gap(9);
 pmutate		= gap(10);
 pgmutate	= gap(11);
 Nregen		= gap(12);

 Lchrom		= fud(97);

 Noofp		= fud(98);
 Noofobj		= fud(99);
 mm		= fud(100:(100+Noofobj-1));

  gno=1;   
  genn(gno)=gno;   
  nooffeval(gno)=gno*(Npop-Nsspop);

  %-----------------------------------------------          
  % Initialize Population   (call fminipop)
  %-----------------------------------------------  
  oldpop=fminipop(gap,fud);   
 
 %-------------------------------  
 % Random PI Initialization    
 %-------------------------------   
 PI=rand(Npop,Noofobj);         
 
 %load ga_warm
 %Ngen=Ngen+Ngen;
   
 %--------   
 % figure   
 %--------      
 if G_disp==1
    FigurePos_GAplot=[70    25   490   350];
    fig_fmga=figure( ...
	'NumberTitle','off', ...
	'Name','flexga Evolution Screen', ...
	'Position',FigurePos_GAplot,...
	'Resize','on', ...
	'Pointer','watch');
    hold off;
end
%--------------------------------------------------------------------------------     
% decode parameters; compute PI by calling the objective function   
%------------------------------------------------------------------------------------------------    
for i=1:Npop   
  [p,PI(i,:),mean_table,sigma_table]=...
  fmdecode(oldpop(i,:),gap,fud,fpd,mean_table,sigma_table,pot_table,fname); % call fmdecode  
  p_p(i,:)=p;   
  PI(i,:)=PI(i,:).*mm;
end % i for loop

savePI=PI;
savep_p=p_p;
if Noofpeaks > 1   
  nichPI=fmnich(fud,Npop,Noofp,PI(:,1),p_p',P_max',P_min',Noofpeaks); 	% call fmnich  
else   
  nichPI=PI;   
end % if   

%-------------------------------------------   
% compute statistics (call fmstats)  
%-------------------------------------------   
[maxi(gno),mini(gno),avg(gno),sd(gno),Nmax,Nmin]=fmstats(PI(:,1));
%-------------------------   
% For each generation    
%-------------------------   
%....................................................................   
%for ijkl=1:Ngen
while gno < Ngen
 
 %--------------------   
 % ga or micro-ga loop   
 %--------------------   
 % -------------------------------------------------------   
 for k=1:Nregen   
  [sort_y,sort_i]=sort(PI);
  %-------------------------------------------------   
  % CALL fmgen.m (generate new population)   
  %-------------------------------------------------   
  [newpop]=fmgen(gap,fud,oldpop,nichPI);
  for i=1:Nsspop   
    newpop((Npop-Nsspop+i),:)=oldpop(sort_i(i),:);   
    PI((Npop-Nsspop+i),:)=savePI(sort_i(i),:);
    p_p((Npop-Nsspop+i),:)=savep_p(sort_i(i),:);
  end % i for loop   
  %-----------------   
  % increment gno   
  %------------------   
  gno=gno+1;    
  %-------------------   
  % decode newpop   
  %-------------------   
  for j=1:(Npop-Nsspop)  
    [p,PI(j,:),mean_table,sigma_table]=...
    fmdecode(newpop(j,:),gap,fud,fpd,mean_table,sigma_table,pot_table,fname);  % call fmdecode  
    p_p(j,:)=p;  
    PI(j,:)=PI(j,:).*mm;
  end % j for loop   

  savePI=PI;   
  savep_p=p_p;

  if Noofpeaks > 1   
    nichPI=fmnich(fud,Npop,Noofp,PI(:,1),p_p',P_max',P_min',Noofpeaks); % call fmnich
  else   
    nichPI=PI;   
  end % if   
  
  genn(gno)=gno;   
  nooffeval(gno)=gno*(Npop-Nsspop);   
  oldpop=newpop;   
  %------------------------------------------   
  % Compute statistics (call fmstats)   
  %------------------------------------------   
  [maxi(gno),mini(gno),avg(gno),sd(gno),Nmax,Nmin]=fmstats(PI(:,1));    
  %------------------------   
  % get best parameters   
  %------------------------   
  bestp = p_p(Nmin,:); 	% call fmdecode.m
  savepop(1,:)=oldpop(Nmin,:);   
   
  if gno > 5, bgno=gno-5; else bgno=1; end   
  genp=genn(bgno:gno);   
  nfnp=nooffeval(bgno:gno);   
  minp=mini(bgno:gno);   
  maxp=maxi(bgno:gno);   
  avgp=avg(bgno:gno);   
  %-------------------------------------------------  
  % PLOT GRAPH & DISPLAY OPTIONS  
  %-------------------------------------------------
     if G_disp==1
       subplot(2,1,1)
       plot(genp,(maxp*mm(1)),'r', genp,(minp*mm(1)),'g',genp,(avgp*mm(1)),'w');    
       title('white = average, green = best, red = worst');   
       xlabel('# of Gens');   
       ylabel('Fitness Value');

      subplot(2,1,2)
      plot(nfnp,(minp*mm(1)),'g'); 
      xlabel('Number of Function Evaluations');   
      ylabel('Fitness Value');
      drawnow
    end
    if G_disp==2
       disp(['#of Gens= ' num2str(gno)]);
    end
    if G_disp==3
       pp1=num2str(gno);
       pp2= num2str(maxi(gno) );
       pp3= num2str(mini(gno) );
       pp4= num2str(avg(gno) );
      disp(['#of Gens =  ' pp1  '   Max =  '  pp2 '   Min =  '  pp3 '  Avg =  ' pp4]);
    end
 end % k for loop (Nregen)   
 %------------------------------------------------------------   
 %----------   
 % Micro GA    
 %----------
 if Nga_choice == 2    
   oldpop=fminipop(gap,fud);   			%call fminipop.m
   oldpop(Npop,:)=savepop(1,:);
   PI(Npop,:)=PI(Nmin,:);
   p_p(Npop,:)=p_p(Nmin,:);   
   for j=1:(Npop-1)  
     [p,PI(j,:),mean_table,sigma_table]=...
      fmdecode(oldpop(j,:),gap,fud,fpd,mean_table,sigma_table,pot_table,fname);  % call fmdecode  
     p_p(j,:)=p;   
     PI(j,:)=PI(j,:).*mm;
   end % j for loop (Npop) 
   savePI=PI;   
   nichPI=PI;
   %------------------------------------------   
   % compute statistics (call fmstats)   
   %------------------------------------------   
   [maxi(gno),mini(gno),avg(gno),sd(gno),Nmax,Nmin]=fmstats(PI);   
 end % if   
 %---------  
 % SAVE  
 %---------
 if Noofpeaks > 1 & gno==Ngen
    [Ysort,Isort]=sort(PI); 
    for i=1:Npop
       bestP(i,:)=p_p(Isort(i),:);
    end   
 else
	bestP=bestp;
 end; % if
% save ga_warm.mat;   
    
end % Ngen for loop   
%.............................................................   
%-----------------------------------------------   
% modify values based on  max. or min.   
%-----------------------------------------------   
min_PI=mini*mm(1);   
max_PI=maxi*mm(1);   
avg_PI=avg*mm(1);
for i=1:Npop
    PI(i,:)=PI(i,:).*mm;
end

%-----------------------------------
% clear old variables if existed
%-----------------------------------
clear oldpop newpop p_p savepop savePI nichPI maxi mini avg sd 
clear fname 

% =========================================================================   
% (c) Flexible Intelligence Group (FIG)   
% END flexga.m   
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