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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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GECCO black-box optimization competitions: progress from 2009 to 2018., , and . GECCO (Companion), page 275-276. ACM, (2019)The Effect of Sampling Methods on the Invariance to Function Transformations When Using Exploratory Landscape Analysis., , and . CEC, page 1139-1146. IEEE, (2021)A complementarity analysis of the COCO benchmark problems and artificially generated problems., , and . GECCO Companion, page 215-216. ACM, (2021)Deep statistics: more robust performance statistics for single-objective optimization benchmarking., , , , , and . GECCO Companion, page 5-6. ACM, (2020)Analyzing the Generalizability of Automated Algorithm Selection: A Case Study for Numerical Optimization., , and . SSCI, page 335-340. IEEE, (2023)CEC Real-Parameter Optimization Competitions: Progress from 2013 to 2018., , and . CEC, page 3126-3133. IEEE, (2019)PerformViz: a machine learning approach to visualize and understand the performance of single-objective optimization algorithms., , , , , and . GECCO Companion, page 7-8. ACM, (2020)Using exploratory landscape analysis to visualize single-objective problems., , and . GECCO Companion, page 27-28. ACM, (2020)A Comprehensive Analysis of the Invariance of Exploratory Landscape Analysis Features to Function Transformations., , and . CEC, page 1-8. IEEE, (2022)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)