My problem has 30-50 integer decision variables, and around 100 nonlinear constraints.
I run it with pupulation size = 300, elite count = 30, function tolerance=1e-11.
Usually, the algorithm stops without finding a feasible solution (exitflag=-2, "average change in the penalty fitness value less than options.FunctionTolerance but constraints are not satisfied")
When I run it many times, with different random numbe seed, it finds a fesible solution which is a local minimun (there are around 10 local minima).
Only if I run it about 50-100 times, it finds the global solution. This approach is very inefficient, since I got a large class of similar problems in structure, but with different numeric data sets.
I played with the parameters (e.g., changed populationsize between 100 to 1000) - there qasn't much change in GA's ability to find a fesible solution in less trials.
I can send the code for your inspection if you give me your email.