All optimization algorithms tend to get stuck in local optima. It's great to try to tune the parameters through experimentation, but he only had enough compute

All optimization algorithms tend to get stuck in local optima. It's great to try to tune the parameters through experimentation, but he only had enough compute time to run the full GA once or twice. Generally I think it's best to use a small population, or even just hillclimbing. But with the smaller population it should run and converge much, much faster. Then you can run it multiple times, and take the best result.