Multiple world genetic algorithm to analyze individually advantageous behaviors in complex networks

Yutaro Miura, Fujio Toriumi, Toshiharu Sugawara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We propose a novel method for evolutionary network analysis that uses the genetic algorithm (GA), called the multiple world genetic algorithm, to coevolve appropriate individual behaviors of many agents on complex networks without sacrificing diversity. We conducted the experiments using simulated games of social networking services to evaluate the proposed method. The results indicate that it could effectively evolve the diverse strategy for each agent and the resulting fitness values were almost always larger than those derived through evolution using the conventional evolutionary network analysis using the GA.

Original languageEnglish
Title of host publicationGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages297-298
Number of pages2
ISBN (Electronic)9781450367486
DOIs
Publication statusPublished - 2019 Jul 13
Event2019 Genetic and Evolutionary Computation Conference, GECCO 2019 - Prague, Czech Republic
Duration: 2019 Jul 132019 Jul 17

Publication series

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

Conference

Conference2019 Genetic and Evolutionary Computation Conference, GECCO 2019
CountryCzech Republic
CityPrague
Period19/7/1319/7/17

Keywords

  • Coevolution
  • Complex networks
  • Diversity
  • Evolutionary network
  • Genetic algorithm
  • Social behavior
  • Social network analysis

ASJC Scopus subject areas

  • Artificial Intelligence
  • Theoretical Computer Science
  • Software

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  • Cite this

    Miura, Y., Toriumi, F., & Sugawara, T. (2019). Multiple world genetic algorithm to analyze individually advantageous behaviors in complex networks. In GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (pp. 297-298). (GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion). Association for Computing Machinery, Inc. https://doi.org/10.1145/3319619.3321989