Does anyone know of any method for deciding when a genetic algorithm is done? In MCMC (e.g, BUGS), several chains are started at different, random points. When they all look the same, it is done. Has this approach ever been tried with GA? Any other ideas?
A simple and common test is to measure improvements in the objective functions: if you no longer improve (by a certain amount) over a set number of iterations, you may as well stop. Other optimisation algorithms use this approach too.