deerstalker

National Labs in the US have been doing this for a while now. I feel like the private sector will take the lead soon.

I have been doing some research on this topic, and found that for some budget regimes (really expensive objective function evaluations) and some applications (HPC code parameter autotuning), the frontier LLMs can even outperform classical optimizers. Even open-weight models can perform well on certain applications but one some they fail abysmally (Of course this is limited to a bunch of niche applications).

Very cool. Evolutionary Algorithms have kinda been out of the mainstream for a long time. They are good when you can do a lot of "black-box" function evaluations but kinda suck when your computational budget is limited. I wonder if coupling them with ML techniques could bring them back.