Evolving to recognize high-dimensional relationships in data: GA operators and representation designed expressly for community detection

Kenneth Smith, Cezary Janikow, Sharlee Climer

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

Abstract

We present a new algorithm for network clustering based upon genetic algorithm methods to optimize modularity. The algorithm proposes an innovative, more abstract representation, along with newly designed domain-specific genetic operators. We then analyze the performance of the algorithm using popular real-world data sets taken from multiple domains. The analysis demonstrates that our algorithm consistently finds high quality or even optimal solutions without any a priori knowledge of the network or the desired number of clusters. Furthermore, we compare our results with five previously published methods and yield the highest quality for the largest of the benchmark datasets.

Original languageEnglish
Title of host publicationGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages308-309
Number of pages2
ISBN (Electronic)9781450367486
DOIs
StatePublished - Jul 13 2019
Externally publishedYes
Event2019 Genetic and Evolutionary Computation Conference, GECCO 2019 - Prague, Czech Republic
Duration: Jul 13 2019Jul 17 2019

Publication series

NameGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion

Conference

Conference2019 Genetic and Evolutionary Computation Conference, GECCO 2019
Country/TerritoryCzech Republic
CityPrague
Period07/13/1907/17/19

Keywords

  • Clustering
  • Community detection
  • Genetic algorithm
  • Modularity

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