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
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

Access to Document

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

bbo-lion7Submitted manuscript, 282 KB

Cite this

@inbook{55bdbd21074a4023a9286a3feaf447c3,

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

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.",

keywords = "Black-box optimization, Fitness landscape analysis, Algorithm portfolio, Computationally intensive functions, Optimization benchmarking, Black-box optimization, Fitness landscape analysis, Algorithm portfolio, Computationally intensive functions, Optimization benchmarking",

author = "Kevin Tierney and Yuri Malitsky and Tinus Abell",

year = "2013",

month = jan,

doi = "10.1007/978-3-642-44973-4\_4",

language = "English",

isbn = "978-3-642-44972-7",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "30--36",

booktitle = "Learning and Intelligent Optimization",

address = "Germany",

note = " Learning and Intelligent OptimizatioN Conference 2013, LION7 ; Conference date: 07-01-2013 Through 11-01-2013",

url = "https://link.springer.com/book/10.1007/978-3-642-44973-4",

}

TY - CHAP

T1 - Features for Exploiting Black-Box Optimization Problem Structure.

AU - Tierney, Kevin

AU - Malitsky, Yuri

AU - Abell, Tinus

PY - 2013/1

Y1 - 2013/1

N2 - 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.

AB - 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.

KW - Black-box optimization

KW - Fitness landscape analysis

KW - Algorithm portfolio

KW - Computationally intensive functions

KW - Optimization benchmarking

KW - Black-box optimization

KW - Fitness landscape analysis

KW - Algorithm portfolio

KW - Computationally intensive functions

KW - Optimization benchmarking

U2 - 10.1007/978-3-642-44973-4_4

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

M3 - Book chapter

SN - 978-3-642-44972-7

T3 - Lecture Notes in Computer Science

SP - 30

EP - 36

BT - Learning and Intelligent Optimization

PB - Springer

T2 - Learning and Intelligent OptimizatioN Conference 2013

Y2 - 7 January 2013 through 11 January 2013

ER -

Features for Exploiting Black-Box Optimization Problem Structure.

Abstract

Conference

Keywords

Access to Document

Fingerprint

Cite this