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Surrogates for hierarchical search spaces: the wedge-kernel and an automated analysis.

, , , and . GECCO, page 916-924. ACM, (2019)

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Surrogates for hierarchical search spaces: the wedge-kernel and an automated analysis., , , and . GECCO, page 916-924. ACM, (2019)Underwater Acoustic Networks for Security Risk Assessment in Public Drinking Water Reservoirs., , , , , , , , , and 1 other author(s). CoRR, (2021)An empirical approach for probing the definiteness of kernels., , and . Soft Comput., 23 (21): 10939-10952 (2019)Resource planning for hospitals under special consideration of the COVID-19 pandemic: optimization and sensitivity analysis., , , , , , , , , and 2 other author(s). GECCO Companion, page 293-294. ACM, (2021)Surrogate models for discrete optimization problems. Dortmund University, Germany, (2018)base-search.net (ftunivdortmund:oai:eldorado.tu-dortmund.de:2003/37870).The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems., , , , , , , , , and 12 other author(s). CoRR, (2022)Models., and . Hyperparameter Tuning for Machine and Deep Learning with R, Springer, (2023)Optimization and Adaptation of a Resource Planning Tool for Hospitals Under Special Consideration of the COVID-19 Pandemic., , , , , , , , , and 3 other author(s). CEC, page 728-735. IEEE, (2021)A First Analysis of Kernels for Kriging-Based Optimization in Hierarchical Search Spaces., and . PPSN (2), volume 11102 of Lecture Notes in Computer Science, page 399-410. Springer, (2018)Case Study IV: Tuned Reinforcement Learning (in Python)., and . Hyperparameter Tuning for Machine and Deep Learning with R, Springer, (2023)