I'm not sure exactly what you are suggesting, but it seems conceptually similar to Bayesian Optimization, which fits a Bayesian Process to previous evaluations

I'm not sure exactly what you are suggesting, but it seems conceptually similar to Bayesian Optimization, which fits a Bayesian Process to previous evaluations of the objective function, and uses this to estimate the best point at which to evaluate it next. This is expensive, so is typically used only when the objective function is itself very expensive to compute, such as for tuning hyperparameters. If you're suggesting training a single neural network to then use as a general optimizer for any problem, you should consider the No Free Lunch Theorem: https://en.wikipedia.org/wiki/No_free_lunch_theorem https://en.wikipedia.org/wiki/No_free_lunch_theorem