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Genetic Algorithm Hybridized with Ruin and Recreate Procedure: Application to the Quadratic Assignment Problem

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Research and Development in Intelligent Systems XIX

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Abstract

Genetic algorithms (GAs) are among the widely used in various areas of computer science, including optimization problems. In this paper, we propose a genetic algorithm hybridized with so-called ruin and recreate (R&R) procedure. We have applied this new hybrid strategy to the well-known combinatorial optimization problem, the quadratic assignment problem (QAP). The results obtained from the experiments on different QAP instances show that the proposed algorithm belongs to the best heuristics for the QAP. The power of this algorithm is also demonstrated by the fact that the new best known solutions were found for several QAP instances.

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Author information

Authors and Affiliations

  1. Department of Practical Informatics, Kaunas University of Technology, Studentų St. 50-416D, LT-3031, 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, FBCS, FIEE, FRSA (Technical Programme Chair) (Technical Programme Chair)

  2. Dept of Computer Science, University of Aberdeen, Aberdeen, UK

    Alun Preece (Deputy Technical Programme Chair) (Deputy Technical Programme Chair)

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

    Frans Coenen (Conference Chairman) (Conference Chairman)

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© 2003 Springer-Verlag London Limited

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Misevicius, A. (2003). Genetic Algorithm Hybridized with Ruin and Recreate Procedure: Application to the Quadratic Assignment Problem. In: Bramer, M., Preece, A., Coenen, F. (eds) Research and Development in Intelligent Systems XIX. Springer, London. https://doi.org/10.1007/978-1-4471-0651-7_12

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  • DOI: https://doi.org/10.1007/978-1-4471-0651-7_12

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-674-5

  • Online ISBN: 978-1-4471-0651-7

  • eBook Packages: Springer Book Archive

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