IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v391y2012i4p1692-1701.html
   My bibliography  Save this article

Scale-free properties of information flux networks in genetic algorithms

Author

Listed:
  • Wu, Jieyu
  • Shao, Xinyu
  • Li, Jinhang
  • Huang, Gang

Abstract

In this study, we present empirical analysis of statistical properties of mating networks in genetic algorithms (GAs). Under the framework of GAs, we study a class of interaction network model—information flux network (IFN), which describes the information flow among generations during evolution process. The IFNs are found to be scale-free when the selection operator uses a preferential strategy rather than a random. The topology structure of IFN is remarkably affected by operations used in genetic algorithms. The experimental results suggest that the scaling exponent of the power-law degree distribution is shown to decrease when crossover rate increases, but increase when mutation rate increases, and the reason may be that high crossover rate leads to more edges that are shared between nodes and high mutation rate leads to many individuals in a generation possessing low fitness. The magnitude of the out-degree exponent is always more than the in-degree exponent for the systems tested. These results may provide a new viewpoint with which to view GAs and guide the dissemination process of genetic information throughout a population.

Suggested Citation

  • Wu, Jieyu & Shao, Xinyu & Li, Jinhang & Huang, Gang, 2012. "Scale-free properties of information flux networks in genetic algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1692-1701.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:4:p:1692-1701
    DOI: 10.1016/j.physa.2011.10.037
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437111008351
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2011.10.037?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Erez Lieberman & Christoph Hauert & Martin A. Nowak, 2005. "Evolutionary dynamics on graphs," Nature, Nature, vol. 433(7023), pages 312-316, January.
    2. Réka Albert & Hawoong Jeong & Albert-László Barabási, 1999. "Diameter of the World-Wide Web," Nature, Nature, vol. 401(6749), pages 130-131, September.
    3. Lambiotte, Renaud & Blondel, Vincent D. & de Kerchove, Cristobald & Huens, Etienne & Prieur, Christophe & Smoreda, Zbigniew & Van Dooren, Paul, 2008. "Geographical dispersal of mobile communication networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5317-5325.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Xiaomin & Yao, Bing, 2020. "Two cumulative distributions for scale-freeness of dynamic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    2. Shang, Ronghua & Bai, Jing & Jiao, Licheng & Jin, Chao, 2013. "Community detection based on modularity and an improved genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1215-1231.
    3. Qi, Jie & Rong, Zhihai, 2013. "The emergence of scaling laws search dynamics in a particle swarm optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1522-1531.
    4. Li, Jun-fang & Zhang, Bu-han & Liu, Yi-fang & Wang, Kui & Wu, Xiao-shan, 2012. "Spatial evolution character of multi-objective evolutionary algorithm based on self-organized criticality theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5490-5499.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Meysam Alizadeh & Claudio Cioffi-Revilla & Andrew Crooks, 2017. "Generating and analyzing spatial social networks," Computational and Mathematical Organization Theory, Springer, vol. 23(3), pages 362-390, September.
    2. Levy, Moshe & Goldenberg, Jacob, 2014. "The gravitational law of social interaction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 418-426.
    3. Levy, Moshe, 2010. "Scale-free human migration and the geography of social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4913-4917.
    4. Mohd-Zaid, Fairul & Kabban, Christine M. Schubert & Deckro, Richard F. & White, Edward D., 2017. "Parameter specification for the degree distribution of simulated Barabási–Albert graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 141-152.
    5. R. Bentley & Michael O’Brien & Paul Ormerod, 2011. "Quality versus mere popularity: a conceptual map for understanding human behavior," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 10(2), pages 181-191, December.
    6. He, He & Yang, Bo & Hu, Xiaoming, 2016. "Exploring community structure in networks by consensus dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 342-353.
    7. Elias Carroni & Paolo Pin & Simone Righi, 2020. "Bring a Friend! Privately or Publicly?," Management Science, INFORMS, vol. 66(5), pages 2269-2290, May.
    8. Duan, Shuyu & Wen, Tao & Jiang, Wen, 2019. "A new information dimension of complex network based on Rényi entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 529-542.
    9. Baek, Seung Ki & Kim, Tae Young & Kim, Beom Jun, 2008. "Testing a priority-based queue model with Linux command histories," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(14), pages 3660-3668.
    10. Freddy Hernán Cepeda López, 2008. "La topología de redes como herramienta de seguimiento en el Sistema de Pagos de Alto Valor en Colombia," Borradores de Economia 513, Banco de la Republica de Colombia.
    11. Chung-Yuan Huang & Chuen-Tsai Sun & Hsun-Cheng Lin, 2005. "Influence of Local Information on Social Simulations in Small-World Network Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-8.
    12. Xue Guo & Hu Zhang & Tianhai Tian, 2019. "Multi-Likelihood Methods for Developing Stock Relationship Networks Using Financial Big Data," Papers 1906.08088, arXiv.org.
    13. Chang, Chia-ling & Chen, Shu-heng, 2011. "Interactions in DSGE models: The Boltzmann-Gibbs machine and social networks approach," Economics Discussion Papers 2011-25, Kiel Institute for the World Economy (IfW Kiel).
    14. Lin, Yi & Zhang, Jianwei & Yang, Bo & Liu, Hong & Zhao, Liping, 2019. "An optimal routing strategy for transport networks with minimal transmission cost and high network capacity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 551-561.
    15. Stefano Breschi & Lucia Cusmano, 2002. "Unveiling the Texture of a European Research Area: Emergence of Oligarchic Networks under EU Framework Programmes," KITeS Working Papers 130, KITeS, Centre for Knowledge, Internationalization and Technology Studies, Universita' Bocconi, Milano, Italy, revised Jul 2002.
    16. He, Xuan & Zhao, Hai & Cai, Wei & Li, Guang-Guang & Pei, Fan-Dong, 2015. "Analyzing the structure of earthquake network by k-core decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 34-43.
    17. Huang, Huilin, 2009. "The degree sequences of an asymmetrical growing network," Statistics & Probability Letters, Elsevier, vol. 79(4), pages 420-425, February.
    18. Gianluca Carnabuci, 2013. "The distribution of technological progress," Empirical Economics, Springer, vol. 44(3), pages 1143-1154, June.
    19. Zhengzheng Pan, 2012. "Opinions and Networks: How Do They Effect Each Other," Computational Economics, Springer;Society for Computational Economics, vol. 39(2), pages 157-171, February.
    20. Laurienti, Paul J. & Joyce, Karen E. & Telesford, Qawi K. & Burdette, Jonathan H. & Hayasaka, Satoru, 2011. "Universal fractal scaling of self-organized networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3608-3613.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:391:y:2012:i:4:p:1692-1701. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.