I WANT TO PASS A INFORMATION OF A BLOCK OF A IMAGE THROUGH binary genetic algorithm and decide it fitness function.what changes i need to make for it.

rRGB1=RGB(:,:,1);

gRGB1=RGB(:,:,2);

bRGB1=RGB(:,:,3);

rRGB=double(rRGB1/255);

gRGB=double(gRGB1/255);

bRGB=double(bRGB1/255);

M = [0.4124564 0.3575761 0.1804375
    0.2126729 0.7151522 0.0721750
    0.0193339 0.1191920 0.9503041] ;

X1 = rRGB  * 0.4124564  + gRGB * 0.3575761  + bRGB*  0.1804375;

Y1 = rRGB  * 0.2126729  + gRGB * 0.7151522 + bRGB*  0.0721750 ;

Z1=  rRGB  *  0.0193339  + gRGB * 0.1191920   + bRGB* 0.9503041 ;

X = X1 / 0.950456;

Y = Y1;

Z = Z1 / 1.088754;

Cb=500 *((X/X1)- (Y/Y1));

Cr= 200 * ((Y/Y1)-(Z/Z1));

s=size(Cb);

n=4; %block size

bs=[n  n];

nb=s./bs;

a_bl=mat2cell(img,repmat(bs(1),1,nb(1)),repmat(bs(2),1,nb(2)));

ff=inline('sum(a_bl,2)');              %objective function

npar=2;                         %number of optimization variables        {set up GA}

varhi=10 ; 

varlo=0;                        %variable limits

maxit=100;                      %max no of iterations

mincost=9999999;                %Maximum allowable cost
                      %GA parameters 

popsize=16;                     % set population size

mutrate=.15;                    % set mutation rate

selection=0.5;                  % fraction of population kept

nbits=8;                        % number of bits in each parameter 

Nt=nbits*npar;                  % total no of bits in a chromosome 

keep=floor(selection*popsize);  %population members that survive(population) 

%Create the initial population

pop=round(rand(popsize,Nt));      % random population of 1’s and 0’s (100 x 8 matrix)

par=(varhi-varlo)*rand(popsize,Nt)+varlo ; %convert binary to continious values

cost=feval(ff,par);                    %calculates population cost using ff

[cost,ind]=sort(cost);                %min cost of element1

par=par(ind,:);pop=pop(ind,:);      %sorts population with lowest cost first

minc(1)=min(cost);                     %min of population 

meanc(1)=mean(cost);                   %meanc contains mean of population 

probs=cost/sum(cost);               % simple normalization for probabilities
                         %iteration 

iga=0;                             %generation counter initilaized

while iga<maxit 

   iga=iga+1;                     %increase of generation counter

   M=ceil((popsize-keep)/2);       %no of matings

Nodds=length(probs);

odds=keep-probs+1; 

pick1=ceil(Nodds*rand(1,M));        %mate #1

pick2=ceil(Nodds*rand(1,M));      %mate #2

%ma & pa contain the idices of the chromosome that will mate 

ma=probs(pick1);

pa=probs(pick2);

%ma & pa contain the idices of the chromosome that will mate 

   ic=1;

while ic<=M

for id=2:keep+1;

if pick1(ic)<=odds(id) && pick1(ic)>odds(id-1)

ma(ic)=id-1;

end

if pick2(ic)<=odds(id) 

pa(ic)=id-1;

else if pick2(ic)>odds(id-1)

pa(ic)=id-1;

end

end 

   end

   ic=ic+1;

end

   cross=ceil((Nt-1)*rand(M,1));      %crossover point

for ic=1:2:M

pop(ceil(M*rand),1:cross)=pop(ic,1:cross);

pop(ceil(M*rand),cross+1:Nt)=pop(ic+1,cross+1:Nt);

pop(ceil(M*rand),1:cross)=pop(ic+1,1:cross);

pop(ceil(M*rand),cross+1:Nt)=pop(ic,cross+1:Nt);

end
% Mutate the population

nmut=ceil((popsize-1)*Nt*mutrate); % total number of mutations

mrow=ceil(rand(1,nmut)*(popsize-1))+1; % row to mutate

mcol=ceil(rand(1,nmut)*Nt); % column to mutate

for ii=1:nmut

pop(mrow(ii),mcol(ii))=abs(pop(mrow(ii),mcol(ii))-1); % toggles bits
end

%The population is re-evaluated for cost

cost=feval(ff,pop); % calculates population cost using fitness function

cost(2:popsize)=feval(ff,par(2:popsize,:));

%Sort the costs and associated parameters

[cost,ind]=sort(cost); % min element in first entry

par=par(ind,:);pop=pop(ind,:);

%Do statistics for a single nonaveraging run

minc(iga+1)=min(cost);

meanc(iga+1)=mean(cost);

probs=cost/sum(cost);
%stopping criteria

if iga>maxit|| cost(1)>mincost 
    break
end

end

but the fitness function is not coming. what is the problem?

[Walter Roberson]

Time to learn how to use the debugger.