[FEATURE] GP surrogate model not repeating the winner evaluation over and over

[aperezdieguez]

Is your feature request related to a problem? Please describe.

Using DeepHyper sequentially with GP and the default Acquisition Function, when it converges it is suggesting the same sample many times.

Describe the solution you'd like

IMHO if you allow the GP re-evaluate the same optimum (because of the noise), I would stop the search after a number of re-evaluations. If the objective function is expensive, you will burn a lot of computing hours on evaluating something you (mostly) already know its result. Instead, you can try to explore some other uncertain regions on the search space: you have likely found the optimum, but if you are going to burn computing hours, at least you are increasing your odds to find a better optimum

Additional context
In my user case, I ran the search with GP in my application. The search converged and the winning candidate was evaluated many times. However, the RF-based search found 5 better optimums than the GP-winning configuration, and when I merged the datasets, I realized that GP didn't evaluate those configurations. There was room to find a better optimum but GP (its acquisition function) suggested the same over and over.