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BBOB Instance Analysis: Landscape Properties and Algorithm Performance Across Problem Instances., , , and . EvoApplications@EvoStar, volume 13989 of Lecture Notes in Computer Science, page 380-395. Springer, (2023)Analyzing the impact of undersampling on the benchmarking and configuration of evolutionary algorithms., , , , and . GECCO, page 867-875. ACM, (2022)Synergizing Theory and Practice of Automated Algorithm Design for Optimization (Dagstuhl Seminar 23332)., , , , and . Dagstuhl Reports, 13 (8): 46-70 (2023)IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics., , , , , and . Evol. Comput., 32 (3): 205-210 (Fall 2024)Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms - Code and Data., , , , , and . (February 2023)Per-Run Algorithm Selection with Warm-starting using Trajectory-based Features - Data., , , , , , and . (April 2022)Deep BIAS: Detecting Structural Bias using Explainable AI., , , and . GECCO Companion, page 455-458. ACM, (2023)Comparing Algorithm Selection Approaches on Black-Box Optimization Problems., , , , , and . GECCO Companion, page 495-498. ACM, (2023)Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization., , , and . GECCO, ACM, (2024)Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks., , , , and . CEC, page 1-8. IEEE, (2025)