There is a different sense of it, specific to PGE, but not anything like stochastic models. In PGE, the local search operators, those that expand equations (parse trees) by making small additions can get into situations where:
1. a pretty good equation has been found
2. small modifications don't have much of an impact, so it stays good
The solution I was thinking of is to do more bookkeeping and eliminate wasted work like this.
In a sense, you don't really get stuck in the same was as GP / Stochastic algos because of the memoization. You always have to be trying new solutions (parse trees)
Also wanted to explore DeepQ/RL for helping to guide the decisions of what to expand and where to expand it.
