ga.m

一个用MATLAB编写的优化控制工具箱

         member_count=member_count+1;
         total=total+fitness(member_count);
      end
      
% Next, make the parent chromosome

    parent_chrom(:,pop_member)=pop(:,member_count);
	end
   end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Reproduce section (i.e., make off-spring - "children")
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% In the approach below each individual gets to mate and
% they randomly pick someone else (not themselves) in the 
% mating pool to mate with.  Resulting children are 
% composed of a combination of genetic material of their
% parents.  If elitism is on, when the elite member gets
% a chance to mate they do not; they are simply copied
% over to the next generation.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

for parent_number1 = 1:POP_SIZE,    % Crossover (parent_number1 is the 
									% individual who gets to mate
	if ELITISM ==1 & parent_number1==bestmember % If elitism on, and 
											 % have the elite member
		child(:,parent_number1)=parent_chrom(:,parent_number1);
	else
	   parent_number2=parent_number1;  % Initialize who the mate is
	   while parent_number2 == parent_number1   % Iterate until find 
		                                        % a mate other than
												% yourself
         parent_number2 = rand*POP_SIZE; % Choose parent number 2
		 								 % randomly (a random mate)
         parent_number2 = parent_number2-rem(parent_number2,1)+1;     
	   end
    if CROSS_PROB > rand           % If true then crossover occurs

         site = rand*CHROM_LENGTH;   % Choose site for crossover
         site = site-rem(site,1)+1;  % and make it a valid integer 
         							 % number for a site

% The next two lines form the child by the swapping of genetic
% material between the parents

child(1:site,parent_number1)=parent_chrom(1:site,parent_number1);

child(site+1:CHROM_LENGTH,parent_number1)=...
     parent_chrom(site+1:CHROM_LENGTH,parent_number2);

      else                           % No crossover occurs

% Copy non-crossovered chromosomes into next generation
% In this case we simply take one parent and make them
% the child.

child(:,parent_number1)=parent_chrom(:,parent_number1);

	 end
   end  % End the "if ELITISM..." statement
end  % End "for parent_number1=..." loop

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Mutate children.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Here, we mutate to a different allele
% with a probability MUTAT_PROB
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

   for pop_member= 1:POP_SIZE,

	if ELITISM ==1 & pop_member==bestmember  % If elitism on, and 
											 % have the elite member
		child(:,pop_member)=child(:,pop_member); % Do not mutate
											% the elite member
	else
	  for site = 1:CHROM_LENGTH,

         if MUTAT_PROB > rand        % If true then mutate  
            rand_gene=rand*10; 		 % Creat a random gene
            
            % If it is the same as the one already there then 
            % generate another random allele in the alphabet
            
            while child(site,pop_member) == rand_gene-rem(rand_gene,1),
               rand_gene=rand*10;
            end;
            
            % If it is not the same one, then mutate
            
            child(site,pop_member)=rand_gene-rem(rand_gene,1);
            
            % If takes a value of 10 (which it cannot
            % mutate to) then try again (this is a very low probability
			% event (most random number generators generate numbers
			% on the *closed* interval [0,1] and this is why this line
			% is included).
            
            if rand_gene == 10
                 site=site-1;
            end
         end  % End "if MUTAT_PROB > rand ... 
	   end  % End for site... loop
	 end  % End "if ELITISM..."
   end  % End for pop_member loop

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Create the next generation (this completes the main part of the GA)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

   pop=child;               % Create next generation (children 
   							% become parents)       

   popcount=popcount+1;		% Increment to the next generation
   
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Next, we have to convert the population (pop) to the base-10 
% representation (called trait) so that we can check if the traits
% all still lie in the proper ranges specified by HIGHTRAIT and LOWTRAIT
% at the beginning of the next time around the loop.  
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

for pop_member = 1:POP_SIZE

	for current_trait = 1:NUM_TRAITS,
			 
trait(current_trait,pop_member,popcount)=0; % Initialize variables
place_pointer=1;

% Change each of the coded traits on the chromosomes into base-10 traits: 
% For each gene on the current_trait past the sign digit but before the
% next trait find its real number amount and hence after finishing
% the next loop trait(current_trait,pop_member,popcount) will be the base-10
% number representing the trait

for gene=TRAIT_START(current_trait)+1:TRAIT_START(current_trait+1)-1,
 place=DECIMAL(current_trait)-place_pointer;
 trait(current_trait,pop_member,popcount)=...
 trait(current_trait,pop_member,popcount)+...
    (pop(gene,pop_member))*10^place;
 place_pointer=place_pointer+1;
end

% Determine sign of the traits and fix 
% trait(current_trait,pop_member,popcount) so that it has the right sign:

if pop(TRAIT_START(current_trait),pop_member) < 5
   trait(current_trait,pop_member,popcount)=...
      -trait(current_trait,pop_member,popcount);
end


	end % Ends "for current_trait=..." loop
         
end    % Ends "for pop_member=..." loop
  			
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Terminate the program when the best fitness has not changed
% more than EPSILON over the last DELTA generations.  It would also
% make sense to use avefitness rather than bestfitness in this test.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

   if popcount > DELTA+1 & ...
       max(abs(bestfitness(popcount-DELTA:popcount-1)-...
          bestfitness(popcount-DELTA-1:popcount-2)))<=EPSILON
       break;
   end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
end  % End "for pop_count=..." loop - the main loop.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Next, provide some plots of the results of the simulation.
% These are for the example function that we seek to maximize; however
% for other applications it is not difficult to modify this code
% to show plots that are informative for the application at hand.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

t=1:popcount-1;  % For use in plotting

x=0:31/100:30;   % For our function the range of values we are considering
y=x;

% Compute the function that we are trying to find the maximum of.

for jj=1:length(x)
	for ii=1:length(y)
		z(ii,jj)=...
		+5*exp(-0.1*((x(jj)-15)^2+(y(ii)-20)^2))...
		-2*exp(-0.08*((x(jj)-20)^2+(y(ii)-15)^2))...
		+3*exp(-0.08*((x(jj)-25)^2+(y(ii)-10)^2))...
		+2*exp(-0.1*((x(jj)-10)^2+(y(ii)-10)^2))...
		-2*exp(-0.5*((x(jj)-5)^2+(y(ii)-10)^2))...
		-4*exp(-0.1*((x(jj)-15)^2+(y(ii)-5)^2))...
		-2*exp(-0.5*((x(jj)-8)^2+(y(ii)-25)^2))...
		-2*exp(-0.5*((x(jj)-21)^2+(y(ii)-25)^2))...
		+2*exp(-0.5*((x(jj)-25)^2+(y(ii)-16)^2))...
		+2*exp(-0.5*((x(jj)-5)^2+(y(ii)-14)^2));
	end
end

% First, show the actual function to be maximized and its contour map

figure(1)
clf
surf(x,y,z);
colormap(jet)
% Use next line for generating plots to put in black and white documents.
colormap(white);
xlabel('x');
ylabel('y');
zlabel('z');
title('Fitness function');

figure(2) 
clf
contour(x,y,z,25)
colormap(jet)
% Use next line for generating plots to put in black and white documents.
colormap(gray);
xlabel('x');
ylabel('y');
zlabel('z');
title('Fitness function (contour map)');

hold on

% Next, add on the evolution of the other population
% members over time.

for pop_member = 1:POP_SIZE
	xx=squeeze(trait(1,pop_member,:));
	yy=squeeze(trait(2,pop_member,:));
	traitplot=plot(xx,yy,'k.');
	set(traitplot,'MarkerSize',6);
end

% Then, add on the evolution of the best individual over 
% time that the GA finds.

btraitplot=plot(bestindividual(1,:),bestindividual(2,:),'ko');
set(btraitplot,'MarkerSize',4)

hold off

% Next, plot some data on the operation of the GA

figure (3)
clf
subplot(211)
plot(t,bestfitness,'k--',t,avefitness,'k-',t,worstfitness,'k-.')
xlabel('Generation')
ylabel('Best, average, and worst fitness')
title('Best and worst fitness vs. generation')
subplot(212)
plot(t,bestindividual(1,:),'k-',t,bestindividual(2,:),'k--')
xlabel('Generation')
ylabel('Best individuals')
title('Best individuals vs. generation')
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% End of program
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%