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A Multilevel Genetic Algorithm for the Maximum Constraint Satisfaction Problem

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Recent Advances in Soft Computing (ICSC-MENDEL 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 576))

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Abstract

The constraint satisfaction problem is a useful and well-studied framework for the modeling of many problems rising in Artificial Intelligence and other areas of Computer Science. As many real-world optimization problems become increasingly complex and hard to solve, better optimization algorithms are always needed. Genetic algorithms (GA) which belongs to the class of evolutionary algorithms is regarded as a highly successful algorithm when applied to a broad range of discrete as well continuous optimization problems. This paper introduces a hybrid approach combining a genetic algorithm with the multilevel paradigm for solving the maximum constraint satisfaction problem. The promising performances achieved by the proposed approach is demonstrated by comparisons made to solve conventional random benchmark problems.

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Authors and Affiliations

  1. Department of Maritime Technology and Innovation, University College of SouthEast, Postboks 235, 3603, Kongsberg, Norway

    Noureddine Bouhmala, Halvard Sanness Helgen & Knut Morten Mathisen

Authors
  1. Noureddine Bouhmala
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  2. Halvard Sanness Helgen
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  3. Knut Morten Mathisen
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Corresponding author

Correspondence to Noureddine Bouhmala .

Editor information

Editors and Affiliations

  1. Department of Applied Computer Science, Institute of Automation and Computer Science, Faculty of Mechanical Engineering, Brno University of Technology, Brno, Czech Republic

    Radek Matoušek

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Bouhmala, N., Helgen, H.S., Mathisen, K.M. (2017). A Multilevel Genetic Algorithm for the Maximum Constraint Satisfaction Problem. In: Matoušek, R. (eds) Recent Advances in Soft Computing. ICSC-MENDEL 2016. Advances in Intelligent Systems and Computing, vol 576. Springer, Cham. https://doi.org/10.1007/978-3-319-58088-3_1

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  • DOI: https://doi.org/10.1007/978-3-319-58088-3_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58087-6

  • Online ISBN: 978-3-319-58088-3

  • eBook Packages: EngineeringEngineering (R0)

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