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The impact of hyper-parameter tuning for landscape-aware performance regression and algorithm selection.

, , , and . GECCO, page 687-696. ACM, (2021)

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Per-run Algorithm Selection with Warm-Starting Using Trajectory-Based Features., , , , , , and . PPSN (1), volume 13398 of Lecture Notes in Computer Science, page 46-60. Springer, (2022)Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization., , , , and . GECCO Companion, page 483-486. ACM, (2023)PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization., , , , , , , , and . AutoML, volume 224 of Proceedings of Machine Learning Research, page 11/1-17. PMLR, (2023)Towards Online Landscape-Aware Algorithm Selection in Numerical Black-Box Optimization. (Vers une sélection en ligne d'algorithmes tenant compte du paysage dans l'optimisation numérique de boîte noire).. Sorbonne University, Paris, France, (2021)Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis., , , , , and . CoRR, (2022)Personalizing performance regression models to black-box optimization problems., , , , and . GECCO, page 669-677. ACM, (2021)Comparing Algorithm Selection Approaches on Black-Box Optimization Problems., , , , , and . GECCO Companion, page 495-498. ACM, (2023)The impact of hyper-parameter tuning for landscape-aware performance regression and algorithm selection., , , and . GECCO, page 687-696. ACM, (2021)Towards Feature-Based Performance Regression Using Trajectory Data., , and . EvoApplications, volume 12694 of Lecture Notes in Computer Science, page 601-617. Springer, (2021)Adaptive landscape analysis., and . GECCO (Companion), page 2032-2035. ACM, (2019)