sga.m

 

NIND = 40;           % Number of individuals per subpopulations
MAXGEN = 300;        % maximum Number of generations
GGAP = .9;           % Generation gap, how many new individuals are created
NVAR = 20;           % Generation gap, how many new individuals are created
PRECI = 20;          % Precision of binary representation

% Build field descriptor
   FieldD = [rep([PRECI],[1, NVAR]); rep([-512;512],[1, NVAR]);...
              rep([1; 0; 1 ;1], [1, NVAR])];

% Initialise population
   Chrom = crtbp(NIND, NVAR*PRECI);

% Reset counters
   Best = NaN*ones(MAXGEN,1);	% best in current population
   gen = 0;			% generational counter

% Evaluate initial population
   ObjV = objfun1(bs2rv(Chrom,FieldD));

% Track best individual and display convergence
   Best(gen+1) = min(ObjV);
   plot(log10(Best),'ro');xlabel('generation'); ylabel('log10(f(x))');
   text(0.5,0.95,['Best = ', num2str(Best(gen+1))],'Units','normalized');   
   drawnow;        


% Generational loop
   while gen < MAXGEN,

    % Assign fitness-value to entire population
       FitnV = ranking(ObjV);

    % Select individuals for breeding
       SelCh = select('sus', Chrom, FitnV, GGAP);

    % Recombine selected individuals (crossover)
       SelCh = recombin('xovsp',SelCh,0.7);

    % Perform mutation on offspring
       SelCh = mut(SelCh);

    % Evaluate offspring, call objective function
       ObjVSel = objfun1(bs2rv(SelCh,FieldD));

    % Reinsert offspring into current population
       [Chrom ObjV]=reins(Chrom,SelCh,1,1,ObjV,ObjVSel);

    % Increment generational counter
       gen = gen+1;

    % Update display and record current best individual
       Best(gen+1) = min(ObjV);
       plot(log10(Best),'ro'); xlabel('generation'); ylabel('log10(f(x))');
       text(0.5,0.95,['Best = ', num2str(Best(gen+1))],'Units','normalized');
       drawnow;
   end 
% End of GA