Benchmarking State-of-the-art DIRECT-type Methods on the BBOB Noiseless Testbed
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Date
2023-07-24
Authors
Kůdela, Jakub
ORCID
0000-0002-4372-2105Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Association for Computing Machinery
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Abstract
In recent years, there has been significant progress in the development of new DIRECT-type algorithms for black-box optimization problems. In this paper, we evaluate three well-performing DIRECT-type methods from a recent extensive numerical study on the BBOB noiseless testbed in dimensions 2, 3, 5, 10, and 20. We discuss the strengths and weaknesses of these algorithms on different classes of functions and provide a comparison with the original DIRECT method, as well as with three other well-established methods: RL-SHADE, L-BFGS-B, and SLSQP.
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Citation
GECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation. 2023, p. 1620-1627.
https://dl.acm.org/doi/abs/10.1145/3583133.3596308
https://dl.acm.org/doi/abs/10.1145/3583133.3596308
Document type
Peer-reviewed
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Published version
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Language of document
en