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

Research output: Conference Article in Proceeding or Book/Report chapter › Book chapter › Research › 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.

Original language	English
Title of host publication	Learning and Intelligent Optimization
Publisher	Springer VS
Publication date	Jan 2013
Pages	30-36
ISBN (Print)	978-3-642-44972-7
DOIs	https://doi.org/10.1007/978-3-642-44973-4_4
Publication status	Published - Jan 2013
Event	Learning and Intelligent OptimizatioN Conference 2013 - Episcopate Museum Catania, Catania, Italy Duration: 7 Jan 2013 → 11 Jan 2013 https://link.springer.com/book/10.1007/978-3-642-44973-4

Conference

Conference	Learning and Intelligent OptimizatioN Conference 2013
Location	Episcopate Museum Catania
Country/Territory	Italy
City	Catania
Period	07/01/2013 → 11/01/2013
Other	Edited by Giuseppe Nicosia and Panos Pardalos
Internet address	https://link.springer.com/book/10.1007/978-3-642-44973-4

Series	Lecture Notes in Computer Science
ISSN	0302-9743

Keywords

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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Cite this

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title = "Features for Exploiting Black-Box Optimization Problem Structure.",

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

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KW - Computationally intensive functions

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Features for Exploiting Black-Box Optimization Problem Structure.

Abstract

Conference

Keywords

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