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MF2: A Collection of Multi-Fidelity Benchmark Functions in Python., and . J. Open Source Softw., 5 (52): 2049 (2020)Finding Efficient Trade-offs in Multi-Fidelity Response Surface Modeling: Generated data files and figures., , , and . (February 2021)Towards an Adaptive CMA-ES Configurator., , and . PPSN (1), volume 11101 of Lecture Notes in Computer Science, page 54-65. Springer, (2018)Towards a theory-guided benchmarking suite for discrete black-box optimization heuristics: profiling (1 + λ) EA variants on onemax and leadingones., , , , and . GECCO, page 951-958. ACM, (2018)Algorithm configuration data mining for CMA evolution strategies., , , and . GECCO, page 737-744. ACM, (2017)Multi-fidelity surrogate model approach to optimization., , , , and . GECCO (Companion), page 225-226. ACM, (2018)IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics., , , , and . CoRR, (2018)Optimizing Highly Constrained Truck Loadings Using a Self-Adaptive Genetic Algorithm., , , and . CEC, page 227-234. IEEE, (2015)Online selection of CMA-ES variants., , , and . GECCO, page 951-959. ACM, (2019)Finding Efficient Trade-offs in Multi-Fidelity Response Surface Modeling., , , and . CoRR, (2021)