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 ProceedingsConference paper (refereed)Referred Conference Paperpeer-review

21 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 languageEnglish
Title of host publicationGECCO 2020 : Proceedings of the 2020 Genetic and Evolutionary Computation Conference
EditorsCarlos Artemio COELLO COELLO
PublisherAssociation for Computing Machinery
Pages620-628
Number of pages9
ISBN (Electronic)9781450371285
DOIs
Publication statusPublished - 26 Jun 2020
Externally publishedYes
Event2020 Genetic and Evolutionary Computation Conference, GECCO 2020 - Cancun, Mexico
Duration: 8 Jul 202012 Jul 2020

Conference

Conference2020 Genetic and Evolutionary Computation Conference, GECCO 2020
Country/TerritoryMexico
CityCancun
Period8/07/2012/07/20

Bibliographical note

Publisher Copyright:
© 2020 ACM.

Funding

Author J. Liu was supported by the National Key R&D Program of China (Grant No. 2017YFC0804003), the National Natural Science Foundation of China (Grant No. 61906083), the Guangdong Provincial Key Laboratory (Grant No. 2020B121201001), the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (Grant No. 2017ZT07X386), the Science and Technology Innovation Committee Foundation of Shenzhen (Grant No. JCYJ20190809121403553), the Shenzhen Science and Technology Program (Grant No. KQTD2016112514355531) and the Program for University Key Laboratory of Guangdong Province (Grant No. 2017KSYS008).

Keywords

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

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