gaunc

function [fval x] = gaunc(fun, lb ,ub)

tic
popSize = 200;
patternTol = 1e-10;
generation = 1000;
stopStall = 100;

nGenome = length(lb);

xover = zeros(popSize, nGenome);
fval = zeros(1,generation);
fmean = zeros(1,generation);

lbRepmat = repmat(lb, 2 * nGenome, 1);
ubRepmat = repmat(ub, 2 * nGenome, 1);
patternVector = [diag(1:length(lb)); -diag(1:length(lb))];

pop = repmat(lb, popSize, 1) + bsxfun(@times, rand(popSize, nGenome), ub - lb);
fitness = fun(pop);
[~, isort] = sort(fitness);
elite = pop(isort(1:2), :);

for g = 1:generation

    rd = unidrnd(2, popSize/2 - 1, nGenome) > 1;
    pop = pop(randperm(popSize), :);
    for i = 1 : popSize/2 - 1
        ri = rd(i, :);
        xover(2*i-1, ri) = pop(2*i-1, ri);
        xover(2*i-1, ~ri) = pop(2*i, ~ri);
        xover(2*i, ~ri) = pop(2*i-1, ~ri);
        xover(2*i, ri) = pop(2*i, ri);
    end

    for j = 1:popSize - 2
        crt = xover(j, :);
        fcrt = fun(crt);
        meshSize = 1;
        while meshSize > patternTol
            next = bsxfun(@plus, crt, meshSize * patternVector);
            infeasibleUB = bsxfun(@gt, next, ub);
            next(infeasibleUB) =  ubRepmat(infeasibleUB);
            infeasibleLB = bsxfun(@lt, next, lb);
            next(infeasibleLB) =  lbRepmat(infeasibleLB);
            [fmin, imin] = min(fun(next));
            if fmin < fcrt
                meshSize = meshSize * 4;
                crt = next(imin, :);
                fcrt = fmin;
            else
                meshSize = meshSize / 4;
            end
        end
        pop(j, :) = crt;
    end

    pop(end-1:end, :) = elite;

    fitness = fun(pop);
    [~, isort] = sort(fitness);
    elite = pop(isort(1:2), :);

    [CurrentMin, k] = min(fitness);
    if g > 1
        if fval(g-1) > CurrentMin
            x = pop(k,:);
            stall = 0;
        else
            stall = stall + 1;
        end
        fval(g) = CurrentMin;
    else
        fval(g) = CurrentMin;
        x = pop(k,:);
        stall = 0;
    end
    fmean(g) = mean(fitness);
    
    if stall == stopStall
        break
    end
end

% shg
% clf reset
% set(gcf, 'color','white', 'menubar','none', 'name','Genetic Algorithm')
% plot(1:g, [fval(1:g); fmean(1:g)], '.')
% legend('Best Fitness','Mean Fitness')
% fval = fval(g);
% title(['Min=',vectorize(vpa(fval))])
% xlabel('Generation')
% ylabel('Fitness Value')
% shg 

toc

end