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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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Discover the Mysteries of the Maya: Selected Contributions from the Machine Learning Challenge & The Discovery Challenge Workshop at ECML PKDD 2021., , , , and . CoRR, (2022)Neurodegenerative Disease Data Ontology., , , , and . DS, volume 11828 of Lecture Notes in Computer Science, page 235-245. Springer, (2019)Linking Problem Landscape Features with the Performance of Individual CMA-ES Modules - Data., , , , , and . (February 2022)Generalization Ability of Feature-Based Performance Prediction Models: A Statistical Analysis Across Benchmarks., , , , and . CEC, page 1-8. IEEE, (2024)Change detection and adaptation in multi-target regression on data streams., , , and . Mach. Learn., 113 (11): 8585-8622 (December 2024)Semantic Description of Data Mining Datasets: An Ontology-Based Annotation Schema., , and . DS, volume 12323 of Lecture Notes in Computer Science, page 140-155. Springer, (2020)Trajectory-based Algorithm Selection with Warm-starting., , , , , and . CEC, page 1-8. IEEE, (2022)OPTION: optimization algorithm benchmarking ontology., , , , , and . GECCO Companion, page 239-240. ACM, (2021)The importance of landscape features for performance prediction of modular CMA-ES variants., , , , , and . GECCO, page 648-656. ACM, (2022)Quantifying Individual and Joint Module Impact in Modular Optimization Frameworks., , , , and . CEC, page 1-8. IEEE, (2024)