20.500.12556/DiRROS-18964 A cross-benchmark examination of feature-based algorithm selector generalization in single-objective numerical optimization The task of selecting the best optimization algorithm for a particular problem is known as algorithm selection (AS). This involves training a model using landscape characteristics to predict algorithm performance, but a key challenge remains: making AS models generalize effectively to new, untrained benchmark suites. This study assesses AS models’ generalizability in single-objective numerical optimization across diverse benchmark suites. Using Exploratory Landscape Analysis (ELA) and transformer-based (TransOpt) features, the research investigates their individual and combined effectiveness in AS across four benchmarks: BBOB, AFFINE, RANDOM, and ZIGZAG. AS models perform differently based on benchmark suite similarities in algorithm performance distributions and single-best solvers. When suites align, these models underperform against a baseline predicting mean algorithm performance; yet, they outperform the baseline when suites differ in performance distributions and solvers. The AS models trained using the ELA landscape features are better than the models trained using the TransOpt features on the BBOB and AFFINE benchmark suites, while the opposite is true for the RANDOM benchmark suite. Ultimately, the study reveals challenges in accurately capturing algorithm performance through problem landscape features (ELA or TransOpt), impacting AS model applicability. algorithm selection multi-target regression generalization benchmarking algoritmi večciljne regresije analize true false true Elsevier Nizozemska Angleški jezik Slovenski jezik © 2024 The Authors. Neznano 2024-05-21 14:16:01 2024-05-21 14:16:01 2024-05-22 03:35:24 0000-00-00 00:00:00 2024 0 0 Opis vira z dne 18. 4. 2024; Nasl. z nasl. zaslona; 1-17 str. Vol. 87 Jun. 2024, [article no.] 101534 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2024-02-06 2024-03-06 2024-04-11 004 2210-6510 10.1016/j.swevo.2024.101534 192973315 https://www.sciencedirect.com/science/article/pii/S2210650224000725?ref=pdf_download&fr=RR-2&rr=88747ba19cc40e85 1 e41322e9-176b-11ef-9c9e-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=26000 RAZ_Cenikj_Gjorgjina_2024.pdf RAZ_Cenikj_Gjorgjina_2024.pdf 1 419C0FFF6D0C63381D3BF48BAB80CFFE adaf1373686fa0668ebec03f4e39afc9b08b16a9a44d7d81c31d10c522361513 6a371733-176d-11ef-9c9e-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=26002 Institut Jožef Stefan 0 0 0