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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)

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Quantifying Individual and Joint Module Impact in Modular Optimization Frameworks., , , , and . CoRR, (2024)Explaining Differential Evolution Performance Through Problem Landscape Characteristics., , , and . BIOMA, volume 13627 of Lecture Notes in Computer Science, page 99-113. Springer, (2022)Sensitivity Analysis of RF+clust for Leave-One-Problem-Out Performance Prediction., , , , and . CEC, page 1-8. IEEE, (2023)Improving Nevergrad's Algorithm Selection Wizard NGOpt Through Automated Algorithm Configuration., , , , , , , , and . PPSN (1), volume 13398 of Lecture Notes in Computer Science, page 18-31. Springer, (2022)Identifying minimal set of Exploratory Landscape Analysis features for reliable algorithm performance prediction., , , , and . CEC, page 1-8. IEEE, (2022)Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarks., , , , and . CoRR, (2024)RF+clust for Leave-One-Problem-Out Performance Prediction., , and . EvoApplications@EvoStar, volume 13989 of Lecture Notes in Computer Science, page 285-301. Springer, (2023)Assessing the Generalizability of a Performance Predictive Model., , , , , , , , and . GECCO Companion, page 311-314. ACM, (2023)Algorithm Instance Footprint: Separating Easily Solvable and Challenging Problem Instances., , , , , and . GECCO, page 529-537. 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)