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A Fitness Approximation Assisted Hyper-heuristic for the Permutation Flowshop Problem

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Advances in Computational Collective Intelligence (ICCCI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1864))

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Abstract

Hyper-heuristics can be applied to solve complex optimization problems. Recently, an efficient hyper-heuristic (HHGA) was proposed for solving the permutation flowshop problem (PFSP), one of the most important scheduling type in modern industries. HHGA is a hyper genetic algorithm (GA) that evolves GAs to solve the PFSP. It designs, automatically, efficient GA per instance. However, HHGA evolves in a huge search space (more than 9 million GAs). Moreover, at each generation, HHGA needs to execute many GAs, which requires a very large number of fitness evaluations; thus, inducing huge computational overhead. To overcome this problem, this paper aims at integrating machine learning techniques into HHGA. The objective is to approximate, in an offline approach, the fitness function, reducing considerably the execution time of HHGA while maintaining its quality. The experimental results on Taillard’s widely used benchmark problems show that the proposed fitness approximation-assisted HHGA is able to achieve competitive performance on a limited computational budget.

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Notes

  1. 1.

    HHGA uses the updates of the upper bound which are available in Taillard’s site; http://mistic.heig-vd.ch/taillard/.

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Authors and Affiliations

  1. University Of Science And Technology Houari Boumediene (USTHB) of Algiers, Algiers, Algeria

    Asma Cherrered & Imene Racha Mekki

  2. Laboratoire des Méthodes de Conception de Systèmes (LMCS), Ecole nationale Supérieure d’Informatique (ESI), BP 68M-16270 Oued Smar, Algiers, Algeria

    Karima Benatchba & Fatima Benbouzid-Si Tayeb

Authors
  1. Asma Cherrered
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  2. Imene Racha Mekki
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  3. Karima Benatchba
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  4. Fatima Benbouzid-Si Tayeb
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Correspondence to Asma Cherrered .

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Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Eötvös Loránd University, Budapest, Hungary

    János Botzheim

  3. Eötvös Loránd University, Budapest, Hungary

    László Gulyás

  4. Universidad Complutense de Madrid, Madrid, Spain

    Manuel Nunez

  5. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Jan Treur

  6. University of Münster, Münster, Germany

    Gottfried Vossen

  7. Wrocław University of Science and Technology, Wrocław, Poland

    Adrianna Kozierkiewicz

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Cherrered, A., Mekki, I.R., Benatchba, K., Benbouzid-Si Tayeb, F. (2023). A Fitness Approximation Assisted Hyper-heuristic for the Permutation Flowshop Problem. In: Nguyen, N.T., et al. Advances in Computational Collective Intelligence. ICCCI 2023. Communications in Computer and Information Science, vol 1864. Springer, Cham. https://doi.org/10.1007/978-3-031-41774-0_42

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  • DOI: https://doi.org/10.1007/978-3-031-41774-0_42

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