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Versatile black-box optimization

Liu, Jialin; Moreau, Antoine; Preuss, Mike; Rapin, Jeremy; Roziere, Baptiste; Teytaud, Fabien; Teytaud, O. (2020), Versatile black-box optimization, GECCO '20: Genetic and Evolutionary Computation Conference, 2020-07, Cancún, Mexico

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Type
Communication / Conférence
Date
2020
Conference title
GECCO '20: Genetic and Evolutionary Computation Conference
Conference date
2020-07
Conference city
Cancún
Conference country
Mexico
Publisher
ACM - Association for Computing Machinery
Published in
New York, NY
ISBN
978-1-4503-7128-5
Pages
620–628
Publication identifier
Metadata
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Author(s)
Liu, Jialin
Moreau, Antoine
Preuss, Mike
Rapin, Jeremy
Roziere, Baptiste
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Teytaud, Fabien
Teytaud, O.
Abstract (EN)
Choosing automatically the right algorithm using problem descriptors is a classical component of combinatorial optimization. It is also a good tool for making evolutionary algorithms fast, robust and versatile. We present Shiwa, an algorithm good at both discrete and continuous, noisy and noise-free, sequential and parallel, black-box optimization. Our algorithm is experimentally compared to competitors on YABBOB, a BBOB comparable testbed, and on some variants of it, and then validated on several real world testbeds.
Subjects / Keywords
Black-box optimization; portfolio algorithm; gradient-free algorithms; open source platfor