The effectiveness of learning hyper-heuristics for other problems, i.e. how much better algorithmically-produced algorithms perform than human-produced algorithms, and more pertinently, where the performance differential (if any) is heading.

As an example, Effective learning hyper-heuristics for the course timetabling problem says: "The dynamic scheme statistically outperforms the static counterpart, and produces competitive results when compared to the state-of-the-art, even producing a new best-known solution. Importantly, our study illustrates that algorithms with increased autonomy and generality can outperform human designed problem-specific algorithms."

Similar results can be found for other problems, bin packing, traveling salesman, and vehicle routing being just some off-the-top-of-my-head examples.