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Combining Genetic Algorithm with the Multilevel Paradigm for the Maximum Constraint Satisfaction Problem

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Machine Learning, Optimization, and Big Data (MOD 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10122))

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Abstract

Genetic algorithms (GA) which belongs to the class of evolutionary algorithms are regarded as highly successful algorithms when applied to a broad range of discrete as well continuous optimization problems. This paper introduces a hybrid approach combining genetic algorithm with the multilevel paradigm for solving the maximum constraint satisfaction problem (Max-CSP). The multilevel paradigm refers to the process of dividing large and complex problems into smaller ones, which are hopefully much easier to solve, and then work backward towards the solution of the original problem, using the solution reached from a child level as a starting solution for the parent level. The promising performances achieved by the proposed approach are 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 of SouthEast, Horten, Norway

    Noureddine Bouhmala

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  1. Noureddine Bouhmala
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Correspondence to Noureddine Bouhmala .

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Editors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  2. Semantic Technology Laboratory, National Research Council (CNR), Catania, Italy

    Piero Conca

  3. Dipartimento di Sociologia e Metodi della Ricerca Sociale, Università di Catania, Catania, Italy

    Giovanni Giuffrida

  4. Department of Mathematics and Computer Science, University of Catania, Catania, Italy

    Giuseppe Nicosia

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Bouhmala, N. (2016). Combining Genetic Algorithm with the Multilevel Paradigm for the Maximum Constraint Satisfaction Problem. In: Pardalos, P., Conca, P., Giuffrida, G., Nicosia, G. (eds) Machine Learning, Optimization, and Big Data. MOD 2016. Lecture Notes in Computer Science(), vol 10122. Springer, Cham. https://doi.org/10.1007/978-3-319-51469-7_28

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  • DOI: https://doi.org/10.1007/978-3-319-51469-7_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51468-0

  • Online ISBN: 978-3-319-51469-7

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