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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.

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pritha Das
pritha Das 2015-5-12
关闭： MATLAB Answer Bot 2021-8-20
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?

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