Exploratory landscape analysis using algorithm based sampling

Yaodong HE; Shiu Yin YUEN; Yang LOU

doi:10.1145/3205651.3205660

Exploratory landscape analysis using algorithm based sampling

Yaodong HE, Shiu Yin YUEN, Yang LOU

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

3 Citations (Scopus)

Abstract

Exploratory landscape analysis techniques are widely used methods for the algorithm selection problem. The existing sampling methods for exploratory landscape analysis are usually designed to sample unbiased candidates for measuring the characteristics of the entire search space. In this paper, we discuss the limitation of the unbiased sampling and propose a novel sampling method, which is algorithm based and thus biased. Based on the sampling method, we propose several novel landscape features which are called algorithm based landscape features. The proposed features are compared with the conventional landscape features using supervised and unsupervised learning. The experimental results show that the algorithm based landscape features outperform the conventional landscape features.

Original language	English
Title of host publication	GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
Editors	Hernan AGUIRRE
Place of Publication	New York, USA
Publisher	Association for Computing Machinery, Inc
Pages	211-212
Number of pages	2
ISBN (Electronic)	9781450357647
DOIs	https://doi.org/10.1145/3205651.3205660
Publication status	Published - 6 Jul 2018
Externally published	Yes
Event	2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan Duration: 15 Jul 2018 → 19 Jul 2018

Conference

Conference	2018 Genetic and Evolutionary Computation Conference, GECCO 2018
Country/Territory	Japan
City	Kyoto
Period	15/07/18 → 19/07/18

Bibliographical note

Funding Information:
This work is supported by Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. CityU 125313].

Publisher Copyright:
© 2018 Copyright held by the owner/author(s).

Keywords

Algorithm based landscape feature
Algorithm selection
Evolutionary algorithm
Exploratory landscape analysis

Access to Document

10.1145/3205651.3205660

Cite this

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title = "Exploratory landscape analysis using algorithm based sampling",

abstract = "Exploratory landscape analysis techniques are widely used methods for the algorithm selection problem. The existing sampling methods for exploratory landscape analysis are usually designed to sample unbiased candidates for measuring the characteristics of the entire search space. In this paper, we discuss the limitation of the unbiased sampling and propose a novel sampling method, which is algorithm based and thus biased. Based on the sampling method, we propose several novel landscape features which are called algorithm based landscape features. The proposed features are compared with the conventional landscape features using supervised and unsupervised learning. The experimental results show that the algorithm based landscape features outperform the conventional landscape features.",

keywords = "Algorithm based landscape feature, Algorithm selection, Evolutionary algorithm, Exploratory landscape analysis",

author = "Yaodong HE and YUEN, {Shiu Yin} and Yang LOU",

note = "Funding Information: This work is supported by Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. CityU 125313]. Publisher Copyright: {\textcopyright} 2018 Copyright held by the owner/author(s).; 2018 Genetic and Evolutionary Computation Conference, GECCO 2018 ; Conference date: 15-07-2018 Through 19-07-2018",

year = "2018",

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doi = "10.1145/3205651.3205660",

language = "English",

pages = "211--212",

editor = "AGUIRRE, { Hernan }",

booktitle = "GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion",

publisher = "Association for Computing Machinery, Inc",

}

HE, Y, YUEN, SY & LOU, Y 2018, Exploratory landscape analysis using algorithm based sampling. in H AGUIRRE (ed.), GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, New York, USA, pp. 211-212, 2018 Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, 15/07/18. https://doi.org/10.1145/3205651.3205660

Exploratory landscape analysis using algorithm based sampling. / HE, Yaodong; YUEN, Shiu Yin; LOU, Yang.

GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion. ed. / Hernan AGUIRRE. New York, USA : Association for Computing Machinery, Inc, 2018. p. 211-212.

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

TY - GEN

T1 - Exploratory landscape analysis using algorithm based sampling

AU - HE, Yaodong

AU - YUEN, Shiu Yin

AU - LOU, Yang

N1 - Funding Information: This work is supported by Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. CityU 125313]. Publisher Copyright: © 2018 Copyright held by the owner/author(s).

PY - 2018/7/6

Y1 - 2018/7/6

N2 - Exploratory landscape analysis techniques are widely used methods for the algorithm selection problem. The existing sampling methods for exploratory landscape analysis are usually designed to sample unbiased candidates for measuring the characteristics of the entire search space. In this paper, we discuss the limitation of the unbiased sampling and propose a novel sampling method, which is algorithm based and thus biased. Based on the sampling method, we propose several novel landscape features which are called algorithm based landscape features. The proposed features are compared with the conventional landscape features using supervised and unsupervised learning. The experimental results show that the algorithm based landscape features outperform the conventional landscape features.

AB - Exploratory landscape analysis techniques are widely used methods for the algorithm selection problem. The existing sampling methods for exploratory landscape analysis are usually designed to sample unbiased candidates for measuring the characteristics of the entire search space. In this paper, we discuss the limitation of the unbiased sampling and propose a novel sampling method, which is algorithm based and thus biased. Based on the sampling method, we propose several novel landscape features which are called algorithm based landscape features. The proposed features are compared with the conventional landscape features using supervised and unsupervised learning. The experimental results show that the algorithm based landscape features outperform the conventional landscape features.

KW - Algorithm based landscape feature

KW - Algorithm selection

KW - Evolutionary algorithm

KW - Exploratory landscape analysis

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M3 - Conference paper (refereed)

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BT - GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion

A2 - AGUIRRE, Hernan

PB - Association for Computing Machinery, Inc

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T2 - 2018 Genetic and Evolutionary Computation Conference, GECCO 2018

Y2 - 15 July 2018 through 19 July 2018

ER -

Exploratory landscape analysis using algorithm based sampling

Abstract

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Bibliographical note

Keywords

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