Exploratory landscape analysis using algorithm based sampling

Yaodong HE, Shiu Yin YUEN, Yang LOU

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

3 Citations (Scopus)


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 languageEnglish
Title of host publicationGECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
Editors Hernan AGUIRRE
Place of PublicationNew York, USA
PublisherAssociation for Computing Machinery, Inc
Number of pages2
ISBN (Electronic)9781450357647
Publication statusPublished - 6 Jul 2018
Externally publishedYes
Event2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan
Duration: 15 Jul 201819 Jul 2018


Conference2018 Genetic and Evolutionary Computation Conference, GECCO 2018

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).


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


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