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GA implementation in matlab without using the toolbox

Asked by Yasmine Tamimi on 22 Apr 2011
Latest activity Edited by Walter Roberson
on 15 Oct 2016

This is my code for implementing ga in matlab:

% Program for Genetic algorithm to minimize the constrained function ga_test
clear all;
clc;
%%  function f = ga_test(x)
%%  f = 120*x1 + 120*x2 + 120*x3 + 120*x4 + 120*x5 + 120*x6 + 120*x7 + 120*x8 + 120*x9 + 120*x10 + 40*y1 + 40*y2 + 40*y3 + 40*y4 + 40*y5 + 40*y6 + 40*y7 + 40*y8 + 40*y9 + 40*y10+ 40*y11 + 40*y12 + 40*y13 + 40*y14 + 40*y15 + 40*y16 + 40*y17 + 40*y18 + 40*y19+ 40*y20;
% Setup the Genetic Algorithm
fitnessfunction= @ga_test;
N = 1310;  % number of optimization (decision) variables
popsize = 8 ; % set population size = number of chromosomes
max_iteration = 50;  % max number of iterations
minimum_cost = 120;  % minimum cost
mutation_rate = 0.01; % mutation rate
selection_rate = 0.5; % selection rate: fraction of population 
nbits = 1;
Nt = nbits*N; % total number of bits in a chormosome
number_mutations = mutation_rate*N*(popsize-1); % number of mutations
% #population members that survive (Nkeep = Xrate*Npop); Nkeep survive for mating, and (Npop - Nkeep) are discarded to make room for the new offspring
keep = floor(selection_rate*popsize); 
iga=0; % generation counter initialized
pop=round(rand(popsize,Nt)); % random population of 1s and 0s
cost=feval(fitnessfunction,pop); % calculates population cost using fitnessfunction
[cost,ind]=sort(cost); % min cost in element 1
pop=pop(ind,:); % sorts population with lowest cost first
minc(1)=min(cost); % minc contains min of population
while iga < max_iteration  %Iterate through generations
iga=iga+1; % increments generation counter
% Pair and mate
M=ceil((M-keep)/2); % number of matings weights chromosomes based upon position in list probability distribution function
prob=flipud([1:keep]'/sum([1:keep])); 
odds=[0 cumsum(prob(1:keep))]; 
pick1=rand(1,popsize); % mate #1
pick2=rand(1,popsize); % mate #2
% parents contain the indicies of the chromosomes 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
if pick2(ic)<=odds(id) & pick2(ic)>odds(id-1)
pa(ic)=id-1;
end % if
end % id
ic=ic+1;
end % while
%_______________________________________________________
% Performs mating using single point crossover
ix=1:2:keep; % index of mate #1
xp=ceil(rand(1,M)*(Nt-1)); % crossover point
pop(keep+ix,:)=[pop(ma,1:xp) pop(pa,xp+1:Nt)];
% first offspring
pop(keep+ix+1,:)=[pop(pa,1:xp) pop(ma,xp+1:Nt)];
% second offspring
%_______________________________________________________
% Mutate the population
number_mutations=ceil((popsize-1)*Nt*mutation_rate); % total number of mutations
mrow=ceil(rand(1,number_mutations)*(popsize-1))+1; % row to mutate
mcol=ceil(rand(1,number_mutations)*Nt); % column to mutate
for ii=1:number_mutations
pop(mrow(ii),mcol(ii))=abs(pop(mrow(ii),mcol(ii))-1);
end 
%_______________________________________________________
% The population is re-evaluated for cost decode
cost(2:popsize)=feval(fitnessfunction,pop(2:popsize,:));
%_______________________________________________________
% Sort the costs and associated parameters
[cost,ind]=sort(cost);
pop=pop(ind,:);
%_______________________________________________________
% Stopping criteria
if iga>maxit | cost(1)<mincost
break
end
[iga cost(1)]
end

I still didn't run my code because I do't know how shall I write my objective function and the constraints?? And upon what shall I decide the popsize?

Any help will be highly appreciated.

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3 Answers

Answer by gurleen Sohi
on 14 May 2011

hi..actuallly i need matlab code for the design of IIR filter i.e to find magnitude and group delay of low pass butterworth filter using genetic algorithm....thanks..reply me on my email gurleensohi@gmail.com

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Please start a new question for that topic.

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Answer by Navneet kaur
on 6 Oct 2016

have you come to know how to remove this error?? @yasmine

  1 Comment

Your objective function should be whatever you want minimized. It will take a vector of input values and must output a scalar score, lower is more "fit".

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Answer by Awais khan
on 15 Oct 2016
Edited by Walter Roberson
on 15 Oct 2016

%problem is to maximize x^2 in 0 and 16 range
%%popsize=5
popsize=6;
POP=round(rand(popsize,4))
%%evaluation of the fitness funcs.
for iteration=1:1000
    for ii=1:popsize
        f(ii)=(bin2dec(num2str(POP(ii, :))))^2;
    end
      for ii=1:popsize
          contribution(ii)=f(ii)/sum(f);
      end
      contribution;
      contribution=contribution* popsize
      contribution=round( contribution)
      %pause
      if sum(contribution)>popsize
          [mymax,myindex]=max( contribution)
          contribution(myindex)= contribution(myindex)-1
          if sum(contribution)>popsize
              [mymax,myindex]=max( contribution)
              contribution(myindex)= contribution(myindex)-1
          end
          %pause
      end
      if sum(contribution)<popsize
          [mymax,myindex]=max( contribution)
          contribution(myindex)= contribution(myindex)+1
          if sum(contribution)<popsize
              [mymax,myindex]=max( contribution)
              contribution(myindex)= contribution(myindex)+1
          end
          %pause
      end%%BURAYI DUZELTELIM
      %myindexes=contribution
      %%Elements to be crossovered
      POP
      pause
      m=1;
      for ii=1:popsize
          if contribution(ii)>0
              gett=contribution(ii);
              for jj=1:gett
                  TOCR(m,:)=POP(gett,:);
                  m=m+1;
              end
          end
      end
      myrandperms=randperm(popsize);
      TOCR
      111
      pause
      TOCR=TOCR(myrandperms,:)
     % pause
      CR=0.7;
      %%CROSSOVER %let crossover point be 2
      for ii=1:2:popsize-1
          myrand=rand;
          if myrand<CR
              temp1=TOCR(ii,3:4);
              temp2=TOCR(ii+1,3:4);
              TOCR(ii,3:4)=temp2;
              TOCR(ii+1,3:4)=temp1;
          end
      end
      PM=0.1;
      %%MUTATION
      for ii=1:popsize
          for jj=1:4%%number of bits
              myrand=rand;
              if myrand<PM
                  TOCR(ii,jj)=TOCR(ii,jj)+1;
                  TOCR(ii,jj)=rem(TOCR(ii,jj),2) ;
              end
          end
      end
      POP=TOCR;
  end
  POP

I have some errors in this code and cannot fix, How can i fix these errors to run my code

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