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Deep statistics: more robust performance statistics for single-objective optimization benchmarking.

, , , , , and . GECCO Companion, page 5-6. ACM, (2020)

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A complementarity analysis of the COCO benchmark problems and artificially generated problems., , and . GECCO Companion, page 215-216. ACM, (2021)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)Understanding the problem space in single-objective numerical optimization using exploratory landscape analysis., , and . Appl. Soft Comput., (2020)Deep statistics: more robust performance statistics for single-objective optimization benchmarking., , , , , and . GECCO Companion, page 5-6. ACM, (2020)PerformViz: a machine learning approach to visualize and understand the performance of single-objective optimization algorithms., , , , , and . GECCO Companion, page 7-8. ACM, (2020)CEC Real-Parameter Optimization Competitions: Progress from 2013 to 2018., , and . CEC, page 3126-3133. IEEE, (2019)Analyzing the Generalizability of Automated Algorithm Selection: A Case Study for Numerical Optimization., , and . SSCI, page 335-340. IEEE, (2023)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)