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Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions., , , and . EvoApplications, volume 12694 of Lecture Notes in Computer Science, page 17-33. Springer, (2021)MATE: A Model-Based Algorithm Tuning Engine - A Proof of Concept Towards Transparent Feature-Dependent Parameter Tuning Using Symbolic Regression., , , , , and . EvoCOP, volume 12692 of Lecture Notes in Computer Science, page 51-67. Springer, (2021)DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems., , , , and . GECCO, page 813-821. ACM, (2023)Optimal static mutation strength distributions for the (1 + λ) evolutionary algorithm on OneMax., and . GECCO, page 660-668. ACM, (2021)Automated configuration of genetic algorithms by tuning for anytime performance: hot-off-the-press track at GECCCO 2022., , , and . GECCO Companion, page 51-52. ACM, (2022)Black-box complexity: from complexity theory to playing mastermind., and . GECCO (Companion), page 617-640. ACM, (2013)Benchmarking and analyzing iterative optimization heuristics with IOHprofiler., , , , , and . GECCO Companion, page 938-945. ACM, (2023)Optimal Parameter Choices Through Self-Adjustment: Applying the 1/5-th Rule in Discrete Settings., and . GECCO, page 1335-1342. ACM, (2015)OneMax in Black-Box Models with Several Restrictions., and . GECCO, page 1431-1438. ACM, (2015)k-Bit Mutation with Self-Adjusting k Outperforms Standard Bit Mutation., , and . PPSN, volume 9921 of Lecture Notes in Computer Science, page 824-834. Springer, (2016)