Versatile black-box optimization

Jialin LIU; Antoine MOREAU; Mike PREUSS; Jeremy RAPIN; Baptiste ROZIÈRE; Fabien TEYTAUD; Olivier TEYTAUD

doi:10.1145/3377930.3389838

Versatile black-box optimization

Jialin LIU, Antoine MOREAU, Mike PREUSS, Jeremy RAPIN, Baptiste ROZIÈRE, Fabien TEYTAUD, Olivier TEYTAUD

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

18 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	GECCO 2020 : Proceedings of the 2020 Genetic and Evolutionary Computation Conference
Editors	Carlos Artemio COELLO COELLO
Publisher	Association for Computing Machinery
Pages	620-628
Number of pages	9
ISBN (Electronic)	9781450371285
DOIs	https://doi.org/10.1145/3377930.3389838
Publication status	Published - 26 Jun 2020
Externally published	Yes
Event	2020 Genetic and Evolutionary Computation Conference, GECCO 2020 - Cancun, Mexico Duration: 8 Jul 2020 → 12 Jul 2020

Conference

Conference	2020 Genetic and Evolutionary Computation Conference, GECCO 2020
Country/Territory	Mexico
City	Cancun
Period	8/07/20 → 12/07/20

Bibliographical note

Publisher Copyright:
© 2020 ACM.

Keywords

Black-box optimization
Gradient-free algorithms
Open source platform
Portfolio algorithm

Access to Document

10.1145/3377930.3389838

Cite this

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title = "Versatile black-box optimization",

abstract = "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.",

keywords = "Black-box optimization, Gradient-free algorithms, Open source platform, Portfolio algorithm",

author = "Jialin LIU and Antoine MOREAU and Mike PREUSS and Jeremy RAPIN and Baptiste ROZI{\`E}RE and Fabien TEYTAUD and Olivier TEYTAUD",

note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 2020 Genetic and Evolutionary Computation Conference, GECCO 2020 ; Conference date: 08-07-2020 Through 12-07-2020",

year = "2020",

month = jun,

day = "26",

doi = "10.1145/3377930.3389838",

language = "English",

pages = "620--628",

editor = "{COELLO COELLO}, {Carlos Artemio}",

booktitle = "GECCO 2020 : Proceedings of the 2020 Genetic and Evolutionary Computation Conference",

publisher = "Association for Computing Machinery",

}

LIU, J, MOREAU, A, PREUSS, M, RAPIN, J, ROZIÈRE, B, TEYTAUD, F & TEYTAUD, O 2020, Versatile black-box optimization. in CA COELLO COELLO (ed.), GECCO 2020 : Proceedings of the 2020 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, pp. 620-628, 2020 Genetic and Evolutionary Computation Conference, GECCO 2020, Cancun, Mexico, 8/07/20. https://doi.org/10.1145/3377930.3389838

Versatile black-box optimization. / LIU, Jialin; MOREAU, Antoine; PREUSS, Mike et al.
GECCO 2020 : Proceedings of the 2020 Genetic and Evolutionary Computation Conference. ed. / Carlos Artemio COELLO COELLO. Association for Computing Machinery, 2020. p. 620-628.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

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T1 - Versatile black-box optimization

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AU - RAPIN, Jeremy

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AU - TEYTAUD, Olivier

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AB - 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.

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KW - Gradient-free algorithms

KW - Open source platform

KW - Portfolio algorithm

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ER -

Versatile black-box optimization

Abstract

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Keywords

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