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The Influence of Population and Memory Sizes on the Evolutionary Algorithm’s Performance for Dynamic Environments

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Applications of Evolutionary Computing (EvoWorkshops 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5484))

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Abstract

Usually, evolutionary algorithms keep the size of the population fixed. In the context of dynamic environments, many approaches divide the main population into two, one part that evolves as usual another that plays the role of memory of past good solutions. The size of these two populations is often chosen off-line. Usually memory size is chosen as a small percentage of population size, but this decision can be a strong weakness in algorithms dealing with dynamic environments. In this work we do an experimental study about the importance of this parameter for the algorithm’s performance. Results show that tuning the population and memory sizes is not an easy task and the impact of that choice on the algorithm’s performance is significant. Using an algorithm that dynamically adjusts the population and memory sizes outperforms standard approach.

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Authors and Affiliations

  1. Department of Informatics and Systems Engineering, Coimbra Polytechnic, Portugal

    Anabela Simões

  2. Centre for Informatics and Systems of the University of Coimbra, Portugal

    Anabela Simões & Ernesto Costa

Authors
  1. Anabela Simões
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  2. Ernesto Costa
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Editor information

Editors and Affiliations

  1. Department of Animal Production Epidemiology and Ecology, University of Torino, Via Leonardo da Vinci 44, 10095, Grugliasco, Italy

    Mario Giacobini

  2. Quinn School of Business, Belfield, University College Dublin, Dublin 4, Ireland

    Anthony Brabazon

  3. Department of Computer Engineering, University of Parma, Viale Usberti 181/a, 43100, Parma, Italy

    Stefano Cagnoni

  4. Dalla Molle Institute for Artificial Intelligence (IDSIA), Gallelria 2, 6928, Manno-Lugano, Switzerland

    Gianni A. Di Caro

  5. School of Engineering and Applied Science,Aston Triangle, Aston University, B4 7ET, Birmingham, UK

    Anikó Ekárt

  6. Complex Adaptive Systems Group, Instituto Tecnológico de Informática, Ciudad PolitŽcnica de la Innovacin, Edif. 8G Acceso B, 46022, Valencia, Spain

    Anna Isabel Esparcia-Alcázar

  7. Next Generation Itelligent Networks Research Center (nexGIN RC), National University of Computer and Emerging Sciences (NUCES), Islamabad, Pakistan

    Muddassar Farooq

  8. Faculty of Economics and Social Sciences, Helmut-Schmidt-University, Holstenhofweg 85, 22043, Hamburg, Germany

    Andreas Fink

  9. Department of Informatics Engineering, Polo II - Pinhal de Marrocos, University of Coimbra, 3030 - 290, Coimbra, Portugal

    Penousal Machado

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Simões, A., Costa, E. (2009). The Influence of Population and Memory Sizes on the Evolutionary Algorithm’s Performance for Dynamic Environments. In: Giacobini, M., et al. Applications of Evolutionary Computing. EvoWorkshops 2009. Lecture Notes in Computer Science, vol 5484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01129-0_80

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  • DOI: https://doi.org/10.1007/978-3-642-01129-0_80

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01128-3

  • Online ISBN: 978-3-642-01129-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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