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📄 sga.m

📁 matlab遗传算法工具箱
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% sga%% 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 NIND = 40;           % Number of individuals per subpopulationsMAXGEN = 300;        % maximum Number of generationsGGAP = .9;           % Generation gap, how many new individuals are createdNVAR = 20;           % Generation gap, how many new individuals are createdPRECI = 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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