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Emergence of New Local Search Algorithms with Neuro-Evolution

  • Conference paper
Evolutionary Computation in Combinatorial Optimization (EvoCOP 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14632))

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Abstract

This paper explores a novel approach aimed at overcoming existing challenges in the realm of local search algorithms. The main objective is to better manage information within these algorithms, while retaining simplicity and generality in their core components. Our goal is to equip a neural network with the same information as the basic local search and, after a training phase, use the neural network as the fundamental move component within a straightforward local search process. To assess the efficiency of this approach, we develop an experimental setup centered around NK landscape problems, offering the flexibility to adjust problem size and ruggedness. This approach offers a promising avenue for the emergence of new local search algorithms and the improvement of their problem-solving capabilities for black-box problems.

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Notes

  1. 1.

    The program source code, benchmark instances and result files are available at the url https://github.com/Salim-AMRI/NK_Landscape_Project.git.

  2. 2.

    For this evaluation test, we only perform one restart per instance, to avoid any dependency between the different executions that might take place on the same instance. It allows to obtain a distribution of 100 independently and identically distributed scores for each strategy and for each NK configuration.

  3. 3.

    The normality condition required for this test was first confirmed using a Shapiro statistical test on the empirical distributions of 100 iid scores obtained by each strategy.

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Acknowledgment

This work was granted access to the HPC resources of IDRIS (Grant No. AD010611887R1) from GENCI. The authors would like to thank the Pays de la Loire region for its financiel support for the Deep Meta project (Etoiles Montantes en Pays de la Loire). The authors also acknowledge ANR - FRANCE (French National Research Agency) for its financial support of the COMBO project (PRC - AAPG 2023 - Axe E.2 - CE23). We are grateful to the reviewers for their comments.

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Authors and Affiliations

  1. Univ Angers, LERIA, SFR MATHSTIC, F-49000, Angers, France

    Olivier Goudet, Mohamed Salim Amri Sakhri, Adrien Goëffon & Frédéric Saubion

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  1. Olivier Goudet
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  2. Mohamed Salim Amri Sakhri
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  3. Adrien Goëffon
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  4. Frédéric Saubion
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Correspondence to Olivier Goudet .

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Editors and Affiliations

  1. IRIDIA, Université libre de Bruxelles, Brussels, Belgium

    Thomas Stützle

  2. Monash University, Clayton, VIC, Australia

    Markus Wagner

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Goudet, O., Amri Sakhri, M.S., Goëffon, A., Saubion, F. (2024). Emergence of New Local Search Algorithms with Neuro-Evolution. In: Stützle, T., Wagner, M. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2024. Lecture Notes in Computer Science, vol 14632. Springer, Cham. https://doi.org/10.1007/978-3-031-57712-3_3

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  • DOI: https://doi.org/10.1007/978-3-031-57712-3_3

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-57711-6

  • Online ISBN: 978-3-031-57712-3

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