This work introduces online LLM-based AAD, a novel optimization paradigm that treats the algorithm itself as a state-dependent decision variable and proposes OnDesign, a multi-agent framework that reconciles competing design perspectives to synthesize executable algorithms and uses execution feedback to refine how runt...
Zhi-Yao Zhang, Yi-Chen Li, Xing-Yu Wu et al.· 0 citations
CausalBN-Bench incorporates background knowledge and training data into prompts to thoroughly unlock LLM'text-comprehension ability during evaluation, whose findings indicate that, LLM understand causality through semantic associations with distinct entities, rather than directly from contextual information or numerica...
Yu Zhou, Xing-Yu Wu, Ji-Bin Wu et al.· IEEE Transactions on Artific...· 22 citations
An uncertainty-aware acquisition rule that combines predicted utility, utility uncertainty, and transition uncertainty to select the next LLM generation action is proposed.
Yu-Hang Wang, Chaoda Peng, Xingyu Wu et al.· 0 citations
LLM-assisted evolutionary search (LES) has emerged as a promising paradigm for automated algorithm design. However, existing methods usually suffer from two inherent limitations when facing the automated design of real-world complex algorithms that usually consist of multiple components. The first limitation is that th...
