📄 sga.m
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% sga.m
%
% This script implements the Simple Genetic Algorithm described
% in the examples section of the GA Toolbox manual.
%
% Author: Andrew Chipperfield
% History: 23-Mar-94 file created
%
% tested under MATLAB v6 by Alex Shenfield (22-Jan-03)
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; % Number of variables
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
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