Source Code MATLAB Algoritma Genetika

% Algoritma Genetika Kontinyu

clear all;clc;

% Set semua parameter pada bagian I, II, and III

%_______________________________________________________

% I Setup the GA

ff='testfunction'; % fungsi objektif

npar=2; % jumlah variable optimasi

varhi=10; varlo=0; % batas variabel

%_______________________________________________________

% II Stopping criteria

maksiterasi=100; % jumlah iterasi maksimum

mincost=-9999999; % cost minimum

%_______________________________________________________

% III GA parameters

jumlahpop=10; % set population size

mutationrate=.2; % set mutation rate

selectionrate=0.5; % populasi yang dipakai (kept)

Nt=npar; % continuous parameter GA Nt=#variables

survive=floor(selectionrate*jumlahpop); % #population

% anggota populasi yang survive

nmut=ceil((jumlahpop-1)*Nt*mutationrate); % total jumlah mutasi

M=ceil((jumlahpop-survive)/2); % jumlah mating

%_______________________________________________________

% Membuat populasi awal

iga=0; % generation counter initialized

par=(varhi-varlo)*rand(jumlahpop,npar)+varlo; % random

cost=feval(ff,par); % hitung cost setiap populasi

[cost,ind]=sort(cost); % min cost di element 1

par=par(ind,:); % sort continuous

minc(1)=min(cost); % minc terdiri dari min dari

meanc(1)=mean(cost); % meanc terdiri dari rata-rata populasi

%_______________________________________________________

% iterasi terus-menerus setiap generasi

while iga<maksiterasi

iga=iga+1;

%_______________________________________________________

% Pair dan mate

M=ceil((jumlahpop-survive)/2); % jumlah mating

prob=flipud([1:survive]'/sum([1:survive])); % weights

% chromosomes

odds=[0 cumsum(prob(1:survive))']; % probabilitas

mate1=rand(1,M); % mate #1

mate2=rand(1,M); % mate #2

% ma dan pa terdiri dari kromosom yang akan disilangkan

ic=1;

while ic<=M

for id=2:survive+1

if mate1(ic)<=odds(id) & mate1(ic)>odds(id-1)

ma(ic)=id-1;

end

if mate2(ic)<=odds(id) & mate2(ic)>odds(id-1)

pa(ic)=id-1;

end

end

ic=ic+1;

end

%_______________________________________________________

ix=1:2:survive;

xp=ceil(rand(1,M)*Nt); % titik crossover

r=rand(1,M); % parameter pencampuran/mixing

for ic=1:M

xy=par(ma(ic),xp(ic))-par(pa(ic),xp(ic)); % ma and pa

% mate

par(survive+ix(ic),:)=par(ma(ic),:); % offspring 1

par(survive+ix(ic)+1,:)=par(pa(ic),:); % offspring 2

par(survive+ix(ic),xp(ic))=par(ma(ic),xp(ic))-r(ic).*xy;

% 1st

par(survive+ix(ic)+1,xp(ic))=par(pa(ic),xp(ic))+r(ic).*xy;

% 2nd

if xp(ic)<npar % crossover ketika variabel terakhir tidak terseleksi

par(survive+ix(ic),:)=[par(survive+ix(ic),1:xp(ic))

par(survive+ix(ic)+1,xp(ic)+1:npar)];

par(survive+ix(ic)+1,:)=[par(survive+ix(ic)+1,1:xp(ic))

par(survive+ix(ic),xp(ic)+1:npar)];

end % if

end

%_______________________________________________________

% Mutate the population

mrow=sort(ceil(rand(1,nmut)*(jumlahpop-1))+1);

mcol=ceil(rand(1,nmut)*Nt);

for ii=1:nmut

par(mrow(ii),mcol(ii))=(varhi-varlo)*rand+varlo;

% mutasi

end % ii

%_______________________________________________________

% offspring baru dan kromosom termutasi dievaluasi

cost=feval(ff,par);

%_______________________________________________________

[cost,ind]=sort(cost);

par=par(ind,:);

%_______________________________________________________

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

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

%_______________________________________________________

% Stopping criteria

if iga>maksiterasi | cost(1)<mincost

break

end

[iga cost(1)]

end

%_______________________________________________________

% Tampilan output

day=clock;

disp(datestr(datenum(day(1),day(2),day(3),day(4),day(5),day(6)),0))

disp(['fungsi optimumnya adalah ' ff])

format short g

disp(['jumlah populasinya adalah= ' num2str(jumlahpop)...

    ' mutation rate nya adalah = ' num2str(mutationrate)...

    ' # par = ' num2str(npar)])

disp(['#generasi ke=' num2str(iga) ' best cost=' num2str(cost(1))])

disp(['solusi terbaik'])

disp([num2str(par(1,:))])

disp('algoritma genetika kontinyu')

figure(1)

iters=0:length(minc)-1;

plot(iters,minc,iters,meanc,'--k');

xlabel('generasi');ylabel('cost');

text(0,minc(1),'best');text(1,minc(2),'populasi rata-rata')

grid on