Features for Exploiting Black-Box Optimization Problem Structure.

Kevin Tierney; Yuri Malitsky; Tinus Abell

doi:10.1007/978-3-642-44973-4_4

Features for Exploiting Black-Box Optimization Problem Structure.

Kevin Tierney, Yuri Malitsky, Tinus Abell

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Bidrag til bog/antologi › Forskning › peer review

Abstract

Black-box optimization (BBO) problems arise in numerous
scientic and engineering applications and are characterized by compu-
tationally intensive objective functions, which severely limit the number
of evaluations that can be performed. We present a robust set of features
that analyze the tness landscape of BBO problems and show how an
algorithm portfolio approach can exploit these general, problem indepen-
dent features and outperform the utilization of any single minimization
search strategy. We test our methodology on data from the GECCO
Workshop on BBO Benchmarking 2012, which contains 21 state-of-the-
art solvers run on 24 well-established functions.

Originalsprog	Engelsk
Titel	Learning and Intelligent Optimization
Forlag	Springer VS
Publikationsdato	jan. 2013
Sider	30-36
ISBN (Trykt)	978-3-642-44972-7
DOI	https://doi.org/10.1007/978-3-642-44973-4_4
Status	Udgivet - jan. 2013
Begivenhed	Learning and Intelligent OptimizatioN Conference 2013 - Episcopate Museum Catania, Catania, Italien Varighed: 7 jan. 2013 → 11 jan. 2013 https://link.springer.com/book/10.1007/978-3-642-44973-4

Konference

Konference	Learning and Intelligent OptimizatioN Conference 2013
Lokation	Episcopate Museum Catania
Land/Område	Italien
By	Catania
Periode	07/01/2013 → 11/01/2013
Andet	Edited by Giuseppe Nicosia and Panos Pardalos
Internetadresse	https://link.springer.com/book/10.1007/978-3-642-44973-4

Navn	Lecture Notes in Computer Science
ISSN	0302-9743

Emneord

Black-box optimization
Fitness landscape analysis
Algorithm portfolio
Computationally intensive functions
Optimization benchmarking

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10.1007/978-3-642-44973-4_4

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Citationsformater

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Features for Exploiting Black-Box Optimization Problem Structure.

Abstract

Konference

Emneord

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