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Optimizing with Low Budgets: a Comparison on the Black-box Optimization Benchmarking Suite and OpenAI Gym., , , , and . CoRR, (2023)Explainable Model-specific Algorithm Selection for Multi-Label Classification., , , , , and . SSCI, page 39-46. IEEE, (2022)Lessons from the black-box: fast crossover-based genetic algorithms., , and . GECCO, page 781-788. ACM, (2013)Analyzing the impact of undersampling on the benchmarking and configuration of evolutionary algorithms., , , , and . GECCO, page 867-875. ACM, (2022)Optimal Parameter Choices via Precise Black-Box Analysis., , and . GECCO, page 1123-1130. ACM, (2016)Adaptive landscape analysis., and . GECCO (Companion), page 2032-2035. ACM, (2019)Fixed-target runtime analysis of the (1 + 1) EA with resampling., , , , and . GECCO (Companion), page 2068-2071. ACM, (2019)Illustrating the trade-off between time, quality, and success probability in heuristic search: a discussion paper., , , and . GECCO (Companion), page 1807-1812. ACM, (2019)Initial design strategies and their effects on sequential model-based optimization: an exploratory case study based on BBOB., , and . GECCO, page 778-786. ACM, (2020)The Impact of Random Initialization on the Runtime of Randomized Search Heuristics., and . Algorithmica, 75 (3): 529-553 (2016)