Reinforced In-Context Black-Box Optimization

arXiv:2402.17423v1 Announce Type: new
Abstract: Black-Box Optimization (BBO) has found successful applications in many fields of science and engineering. Recently, there has been a growing interest in meta-learning particular components of BBO algorithms to speed up optimization and get rid of tedious hand-crafted heuristics. As an extension, learning the entire algorithm from data requires the least labor from experts and can provide the most flexibility. In this paper, we propose RIBBO, a method to reinforce-learn a BBO algorithm from offline …

abstract algorithm algorithms applications arxiv box components context cs.ai cs.lg cs.ne data engineering extension fields found heuristics least meta meta-learning optimization science speed type