Exploratory landscape analysis using algorithm based sampling

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

4 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationGECCO '18 Proceedings of the Genetic and Evolutionary Computation Conference Companion
EditorsHernan Aguirre
PublisherACM New York
Pages211-212
ISBN (Electronic)978-1-4503-5764-7
ISBN (Print)9781450357647
Publication statusPublished - 17 Jul 2018

Conference

TitleThe Genetic and Evolutionary Computation Conference 2018
Location
PlaceJapan
CityKyoto
Period15 - 19 July 2018

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.

Research Area(s)

  • Algorithm based landscape feature, Algorithm selection, Evolutionary algorithm, Exploratory landscape analysis

Citation Format(s)

Exploratory landscape analysis using algorithm based sampling. / He, Yaodong; Yuen, Shiu Yin ; Lou, Yang.

GECCO '18 Proceedings of the Genetic and Evolutionary Computation Conference Companion. ed. / Hernan Aguirre. ACM New York, 2018. p. 211-212.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review