FTe = randn() * 10;
FTi = randn() * 10;
H = @(xy) 0.5 * (xy(1) - FTe).^2 .* 0.5*(xy(2) - FTi).^2;
good_enough = 0.01;
nvar = 2;
A = []; b = [];
Aeq = []; beq = [];
lb = zeros(1, nvar);
ub = ones(1, nvar);
nlcon = [];
options = optimoptions('ga', 'display', 'iter', 'FitnessLimit', good_enough);
[xy, fval] = ga(H, nvar, A, b, Aeq, beq, lb, ub, nlcon, options )
ga does not care whether the formula gives 0: ga will keep minimizing until either the target fitness is reached (if one was specified), or until it decide that there have been no improvements for a sufficiently long time, or until it has executed the function more times that it has been configured to try. It is just that function value 0 (such as a residue function) or function value -infinity are the two common cases for knowing when to stop: if you do not know what the global minima is for your objective function, then it is hard to know if ga did a good enough job.
With the random FTe and FTi that I put in for this sample code, if the random values are outside of the lower bound to upper bound range, ga will keep bouncing off the ends of the ranges until it gives up. If, though, the random FTe and FTi happen to be in the 0 to 1 range, then with that particular formula, ga will need only a small number of iterations to reach the target you asked for.
