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Restart-Based Genetic Algorithm for the Quadratic Assignment Problem

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Research and Development in Intelligent Systems XXV (SGAI 2008)

Abstract

The power of genetic algorithms (GAs) has been demonstrated for various domains of the computer science, including combinatorial optimization. In this paper, we propose a new conceptual modification of the genetic algorithm entitled a “restart-based genetic algorithm” (RGA). An effective implementation of RGA for a well-known combinatorial optimization problem, the quadratic assignment problem (QAP), is discussed. The results obtained from the computational experiments on the QAP instances from the publicly available library QAPLIB show excellent performance of RGA. This is especially true for the real-life like QAPs.

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Author information

Authors and Affiliations

  1. Kaunas University of Technology, Studentu st. 50-400a/416a, LT-51368, Kaunas, Lithuania

    Alfonsas Misevicius

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  1. Alfonsas Misevicius
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Editor information

Editors and Affiliations

  1. Faculty of Technology, University of Portsmouth, Portsmouth, UK

    Max Bramer BSc, PhD, CEng, CITP, FBCS, FIET, FRSA, FHEA

  2. University of Greenwich, UK

    Miltos Petridis DipEng, MBA, PhD, MBCS, AMBA

  3. Department of Computer Science, University of Liverpool, Liverpool, UK

    Frans Coenen BSc, PhD

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© 2009 Springer-Verlag London Limited

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Misevicius, A. (2009). Restart-Based Genetic Algorithm for the Quadratic Assignment Problem. In: Bramer, M., Petridis, M., Coenen, F. (eds) Research and Development in Intelligent Systems XXV. SGAI 2008. Springer, London. https://doi.org/10.1007/978-1-84882-171-2_7

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  • DOI: https://doi.org/10.1007/978-1-84882-171-2_7

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84882-170-5

  • Online ISBN: 978-1-84882-171-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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