What you describe is called local optima and it's well studied. There is no magic way around it, you just need to put resources into exploring a larger area. Me

What you describe is called local optima and it's well studied. There is no magic way around it, you just need to put resources into exploring a larger area. Methods include selecting really diverse solutions rather than just the best one, restarting the simulation with a new random population, or sometimes throwing away the best solution and letting it wander around randomly longer before converging. >I suspect that a solution lies in work on neural nets where one net is trained, then a second, smaller, net is trained to match the first. This is a form of "abstraction". Somewhere in that space is something very important to AI. Neural nets have a lot of desirable properties for this kind of stuff. Mainly that they can be trained with reinforcement learning. Instead of randomly testing which solutions work better, you have them try to predict how good each action is.