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

Published: 13 July 2019 Publication History

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.

References

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Charu Aggarwal and Karthik Subbian. 2014. Evolutionary Network Analysis: A Survey. ACM Comput. Surv. 47, 1, Article 10 (May 2014), 36 pages
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Robert Axelrod. 1986. An evolutionary approach to norms. American political science review 80, 4 (1986), 1095--1111.
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Payel Ghosh and Melanie Mitchell. 2006. Segmentation of medical images using a genetic algorithm. In Proc. of the 8th Ann. Conf. on Genetic and Evolutionary Computation. ACM, 1171--1178.
[4]
Yutaro Miura, Fujio Toriumi, and Toshiharu Sugawara. 2018. Evolutionary Learning Model of Social Networking Services with Diminishing Marginal Utility. In Companion Proceedings of the The Web Conference 2018 (WWW '18). 1323--1329.
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Fujio Toriumi, Hitoshi Yamamoto, and Isamu Okada. 2012. Why do people use social media? Agent-based simulation and population dynamics analysis of the evolution of cooperation in social media. In Proc. of the IEEE/WIC/ACM Int. J. Conf. on Web Intelligence and Intelligent Agent Technology-Volume 02. 43--50.

Cited By

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  • (2021)Modeling and analyzing users’ behavioral strategies with co-evolutionary processComputational Social Networks10.1186/s40649-021-00092-18:1Online publication date: 10-Mar-2021
  • (2019)Analysis of Diversity and Dynamics in Co-evolution of Cooperation in Social Networking ServicesComplex Networks and Their Applications VIII10.1007/978-3-030-36687-2_41(495-506)Online publication date: 26-Nov-2019

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Published In

cover image ACM Conferences
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2019
2161 pages
ISBN:9781450367486
DOI:10.1145/3319619
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2019

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Author Tags

  1. coevolution
  2. complex networks
  3. diversity
  4. evolutionary network
  5. genetic algorithm
  6. social behavior
  7. social network analysis

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GECCO '19
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GECCO '19: Genetic and Evolutionary Computation Conference
July 13 - 17, 2019
Prague, Czech Republic

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2021)Modeling and analyzing users’ behavioral strategies with co-evolutionary processComputational Social Networks10.1186/s40649-021-00092-18:1Online publication date: 10-Mar-2021
  • (2019)Analysis of Diversity and Dynamics in Co-evolution of Cooperation in Social Networking ServicesComplex Networks and Their Applications VIII10.1007/978-3-030-36687-2_41(495-506)Online publication date: 26-Nov-2019

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