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

Yutaro Miura, Fujio Toriumi, Toshiharu Sugawara

研究成果: Conference contribution

1 引用 (Scopus)

抄録

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.

元の言語English
ホスト出版物のタイトルGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
出版者Association for Computing Machinery, Inc
ページ297-298
ページ数2
ISBN(電子版)9781450367486
DOI
出版物ステータスPublished - 2019 7 13
イベント2019 Genetic and Evolutionary Computation Conference, GECCO 2019 - Prague, Czech Republic
継続期間: 2019 7 132019 7 17

出版物シリーズ

名前GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion

Conference

Conference2019 Genetic and Evolutionary Computation Conference, GECCO 2019
Czech Republic
Prague
期間19/7/1319/7/17

Fingerprint

Complex networks
Complex Networks
Genetic algorithms
Genetic Algorithm
Network Analysis
Electric network analysis
Social Networking
Fitness
Game
Evaluate
Experiment
Experiments

ASJC Scopus subject areas

  • Artificial Intelligence
  • Theoretical Computer Science
  • Software

これを引用

Miura, Y., Toriumi, F., & Sugawara, T. (2019). Multiple world genetic algorithm to analyze individually advantageous behaviors in complex networks. : 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

Multiple world genetic algorithm to analyze individually advantageous behaviors in complex networks. / Miura, Yutaro; Toriumi, Fujio; Sugawara, Toshiharu.

GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, 2019. p. 297-298 (GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion).

研究成果: Conference contribution

Miura, Y, Toriumi, F & Sugawara, T 2019, Multiple world genetic algorithm to analyze individually advantageous behaviors in complex networks. : GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion. GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, Inc, pp. 297-298, 2019 Genetic and Evolutionary Computation Conference, GECCO 2019, Prague, Czech Republic, 19/7/13. https://doi.org/10.1145/3319619.3321989
Miura Y, Toriumi F, Sugawara T. Multiple world genetic algorithm to analyze individually advantageous behaviors in complex networks. : GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc. 2019. p. 297-298. (GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion). https://doi.org/10.1145/3319619.3321989
Miura, Yutaro ; Toriumi, Fujio ; Sugawara, Toshiharu. / Multiple world genetic algorithm to analyze individually advantageous behaviors in complex networks. GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, 2019. pp. 297-298 (GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion).
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