arXiv:2606. 02016v1 Announce Type: new Abstract: Algorithm Selection (AS) aims to automatically identify the most suitable optimization algorithm for a given problem instance by leveraging measurable problem characteristics and historical performance data.
By Gjorgjina Cenikj, Jakub Kudela, Eva Tuba, Tome Eftimov
arXiv:2602.00532v2 Announce Type: replace-cross
Abstract: Constraint handling is central to constrained black-box optimization (BBO), where objective improvement and feasibility restoration often pro...
By Sijie Ma, Zeyuan Ma, Yue-Jiao Gong, Ran Cheng
arXiv:2605. 04954v2 Announce Type: replace-cross Abstract: Per-instance algorithm selection (PIAS) takes advantage of complementarity between a set of algorithms by deciding which algorithm to run on a given instance.
By Koen van der Blom, Diederick Vermetten
arXiv:2607. 27389v1 Announce Type: new Abstract: Learning-to-optimize (L2O) methods accelerate repeated optimization by training models to predict solutions, warm starts, branching decisions, or other forms of solver guidance.
By Bingheng Li, Junyang Cai, Yupeng Zhang, Bistra Dilkina, Jayant Kalagnanam, Dzung T. Phan
Continuous-time (CT) machine learning has emerged as a principled framework for modeling temporal dynamics as a continuous process, particularly when observations are sampled at arbitrary time points...
arXiv:2609.01493v1 Announce Type: cross
Abstract: Black-Box Optimization (BBO) has found broad applications, but evolutionary algorithms and Bayesian optimization face efficiency challenges as real-w...
By Chao Qian, Chen-Guang Wang, Rong-Xi Tan, Ke Xue