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Spatially Structured Evolutionary Algorithms: Graph Degree, Population Size and Convergence Speed

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Intelligent Distributed Computing XI (IDC 2017)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 737))

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Abstract

An evolutionary algorithm (EA) is said to be spatially structured when its individuals are arranged in an incomplete graph and interact only with their neighbors. Previous studies argue that spatially structured EAs are less likely to converge prematurely to local optima. Furthermore, they have been initially designed for distributed computing and it is often claimed that their parallelization is simpler than the equivalent non-structured algorithm. However, most of the empirical studies on spatially structured EAs use a predefined and fixed population size, whereas the full potential of this or any other any kind of EA can only be explored if the population size is properly set. This paper investigates optimal population sizes of spatially structured EAs (cellular EAs, in particular) and the relationship between that size, convergence speed and the degree of the structuring network. EAs structured by regular graphs with different degrees have been tested on different types of fitness landscapes. We conclude that in most cases graphs with low degree require smaller populations to converge consistently to global optima. However, if the population size is properly set, EAs structured by graphs with higher degrees not only converge to global optima with high probability, but also converge faster.

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Acknowledgements

First author wishes to thank FCT, Ministério da Ciência e Tecnologia, his Research Fellowship SFRH/BPD/111065/2015). This work was supported by FCT PROJECT [UID/EEA/50009/2013].

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Authors and Affiliations

  1. LARSyS: Laboratory for Robotics and Systems in Engineering and Science, University of Lisbon, Lisbon, Portugal

    Carlos M. Fernandes & Agostinho C. Rosa

  2. Department of Computer Architecture, University of Granada, Granada, Spain

    Carlos M. Fernandes

  3. LITIS, University of Le Havre, Le Havre, France

    Juan L. J. Laredo

Authors
  1. Carlos M. Fernandes
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  2. Juan L. J. Laredo
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  3. Agostinho C. Rosa
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Corresponding author

Correspondence to Carlos M. Fernandes .

Editor information

Editors and Affiliations

  1. Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Novi Sad, Serbia

    Mirjana Ivanović

  2. Faculty of Automatics, Computer Science and Electronics, University of Craiova, Craiova, Romania

    Costin Bădică

  3. Institut für Informatik, Technische Universität Clausthal, Clausthal-Zellerfeld, Germany

    Jürgen Dix

  4. School of Electrical Engineering, University of Belgrade, Belgrade, Serbia

    Zoran Jovanović

  5. Dipartimento di Ingegneria Elettrica, Elettronica e Informatica, Universita degli Studi di Catania, Catania, Italy

    Michele Malgeri

  6. Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Novi Sad, Serbia

    Miloš Savić

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Fernandes, C.M., Laredo, J.L.J., Rosa, A.C. (2018). Spatially Structured Evolutionary Algorithms: Graph Degree, Population Size and Convergence Speed. In: Ivanović, M., Bădică, C., Dix, J., Jovanović, Z., Malgeri, M., Savić, M. (eds) Intelligent Distributed Computing XI. IDC 2017. Studies in Computational Intelligence, vol 737. Springer, Cham. https://doi.org/10.1007/978-3-319-66379-1_2

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  • DOI: https://doi.org/10.1007/978-3-319-66379-1_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66378-4

  • Online ISBN: 978-3-319-66379-1

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