I dunno how you'd use genetic algorithms, since solutions are paths, and there's usually no way to splice segments of candidate paths together. This kind of pr

I dunno how you'd use genetic algorithms, since solutions are paths, and there's usually no way to splice segments of candidate paths together. This kind of problem is known as planning in old AI, and it's languishing. most techniques are just variations of A*, with things like symmetry detection and heuristics tailored to game benchmark problems. It's the kind of thing (IMO) that deep RL fails at, since unlike Chess or Go where your solution-finding simply needs to beat a competitor, here your solution needs to be exactly correct (and ideally minimal.) It's very sparse.