Genetic Algorithm not finding global minimum

Hello,

I am running an optimization problem with integer variables to which I know there are better global minimums than what the ga function is finding. I have already changed the generations and stall stopping criteria, but in every run, the algorithm finds different local minimum, gets stuck with it and neglects the other possible minimums. I watch the best individual plot while it is running and can see this happening.

My guess is that mutations aren't happening often enough.

Is there any way to solve this? Is this just the case to increase even more the generations and stall stopping criteria?

Regards, Ian

[Star Strider]

I generally begin with a very large, random initial population, for example:

randi(99, 500, number_of_parameters)

Although that can take a while longer, it generally converges on the correct result.

[Ian Bortolotti Gomes]

I got some help from my professor and he advised me to incorporate my constraint into the fitness function and not to use the non-linear constraint function parameter from the ga function. This seems to have done the trick.