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Metaheuristics International Conference

MIC 2022: Metaheuristics pp 118–132Cite as

Unsupervised Machine Learning for the Quadratic Assignment Problem

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13838))

Abstract

An unsupervised machine learning method based on association rule is studied for the Quadratic Assignment Problem. Parallel extraction of itemsets and local search algorithms are proposed. The extraction of frequent itemsets in the context of local search is shown to produce good results for a few problem instances. Negative results of the proposed learning mechanism are reported for other instances. This result contrasts with other hard optimization problems for which efficient learning processes are known in the context of local search.

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Author information

Authors and Affiliations

  1. University of Lausanne, Service de la recherche, Bâtiment Amphipôle, CH-1015, Lausanne, Switzerland

    Thé Van Luong

  2. University of Applied Sciences and Arts of Western Switzerland, HEIG-VD, Department of Information and Communication Technologies, Route de Cheseaux 1, CH-1401, Yverdon-les-Bains, Switzerland

    Éric D. Taillard

Authors
  1. Thé Van Luong
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  2. Éric D. Taillard
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Correspondence to Éric D. Taillard .

Editor information

Editors and Affiliations

  1. University of Udine, Udine, Italy

    Luca Di Gaspero

  2. University of Napoli - Federico II, Napoli, Italy

    Paola Festa

  3. University of Paris Est, Paris, France

    Amir Nakib

  4. University of Catania, Catania, Italy

    Mario Pavone

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Luong, T.V., Taillard, É.D. (2023). Unsupervised Machine Learning for the Quadratic Assignment Problem. In: Di Gaspero, L., Festa, P., Nakib, A., Pavone, M. (eds) Metaheuristics. MIC 2022. Lecture Notes in Computer Science, vol 13838. Springer, Cham. https://doi.org/10.1007/978-3-031-26504-4_9

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  • DOI: https://doi.org/10.1007/978-3-031-26504-4_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26503-7

  • Online ISBN: 978-3-031-26504-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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