3 papers
cs.LG2026
Large Language Model as Meta-Surrogate for Data-Driven Many-Task Optimization: A Proof-of-Principle Study
Xian-Rong Zhang, Yue-Jiao Gong, Yuan-Ting Zhong +2
In many-task optimization scenarios, surrogate models are valuable for mitigating the computational burden of repeated fitness evaluations across tasks. This study proposes a novel…
cs.NE2026
Detect and Act: Automated Dynamic Optimizer through Meta-Black-Box Optimization
Zijian Gao, Yuanting Zhong, Zeyuan Ma +2
Dynamic Optimization Problems (DOPs) are challenging to address due to their complex nature, i.e., dynamic environment variation. Evolutionary Computation methods are generally adv…
cs.LG2025
TRACE: A Generalizable Drift Detector for Streaming Data-Driven Optimization
Yuan-Ting Zhong, Ting Huang, Xiaolin Xiao +1
Many optimization tasks involve streaming data with unknown concept drifts, posing a significant challenge as Streaming Data-Driven Optimization (SDDO). Existing methods, while lev…