Reviews: Demystifying Black-box Models with Symbolic Metamodels

I am new to the domain of symbolic regression and found the article to constitute a well-written and interesting introduction to it. Yet, I kept wondering to what extent the presented approach can really help interpreting complex black box functions. In the final example, it is clear that the results are fairly simple and interpretable while delivering a moderate loss in prectivity compared to the crude algorithm. But in more generality, I still don't see how combinations of Bessel functions and alike will help most practitioners. Which leads us to a question that to the best of my understanding was somehow underinvestigated here, namely some more systematic approach on how to tune the complexity of the metamodel, and maybe explore the Pareto front of simplicity versus predictivity.