EBIC: A next-generation evolutionary-based parallel biclustering method

Patryk Orzechowski, Xiuzhen Huang, Moshe Sipper, Jason H. Moore

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

In this paper we present the recent accomplishments in developing a biclustering method based on evolutionary computation. In one of the recent papers we demonstrated the supremacy of our method over several state-of-the-art algorithms. We highlight the evolutionary fundamentals of the method and discuss potential future directions.

Original languageEnglish
Title of host publicationGECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages59-60
Number of pages2
ISBN (Electronic)9781450357647
DOIs
StatePublished - 6 Jul 2018
Externally publishedYes
Event2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan
Duration: 15 Jul 201819 Jul 2018

Publication series

NameGECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion

Conference

Conference2018 Genetic and Evolutionary Computation Conference, GECCO 2018
Country/TerritoryJapan
CityKyoto
Period15/07/1819/07/18

Keywords

  • Biclustering
  • Data mining
  • Evolutionary computation
  • Genetic programming
  • Unsupervised machine learning

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