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Cherry-Picking Meta-heuristic Algorithms and Parameters for Real Optimization Problems

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Progress in Artificial Intelligence (EPIA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13566))

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Abstract

We present an approach that is able to automatically choose the best meta-heuristic and configuration for solving a real optimization problem. Our approach allows the researcher to indicate which meta-heuristics to choose from and, for each meta-heuristic, which parameters should be automatically configured to find good solutions for the optimization problem. We show that our approach is sound using ten well know real optimization problems and five meta-heuristics. As a side effect, we were also able to provide an unbiased way of assessing meta-heuristics concerning their performance to address one or more classes of real optimization problems. Our approach improved the results found for all the meta-heuristics in all problems and was also able to find very competitive results for all optimization problems when given the liberty to choose which meta-heuristic to use.

This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020. Kevin Martins thanks FCT for the grant SFRH/BD/151434/2021.

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Authors and Affiliations

  1. University of Minho, Braga, Portugal

    Kevin Martins & Rui Mendes

Authors
  1. Kevin Martins
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  2. Rui Mendes
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Corresponding author

Correspondence to Rui Mendes .

Editor information

Editors and Affiliations

  1. ISEP/GECAD, Polytechnic Institute of Porto, Porto, Portugal

    Goreti Marreiros

  2. IST/INESC-ID, University of Lisbon, Lisbon, Portugal

    Bruno Martins

  3. IST/INESC-ID, University of Lisbon, Porto Salvo, Portugal

    Ana Paiva

  4. CISUC, University of Coimbra, Coimbra, Portugal

    Bernardete Ribeiro

  5. IST/INESC-ID, University of Lisbon, Porto Salvo, Portugal

    Alberto Sardinha

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Martins, K., Mendes, R. (2022). Cherry-Picking Meta-heuristic Algorithms and Parameters for Real Optimization Problems. In: Marreiros, G., Martins, B., Paiva, A., Ribeiro, B., Sardinha, A. (eds) Progress in Artificial Intelligence. EPIA 2022. Lecture Notes in Computer Science(), vol 13566. Springer, Cham. https://doi.org/10.1007/978-3-031-16474-3_41

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  • DOI: https://doi.org/10.1007/978-3-031-16474-3_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16473-6

  • Online ISBN: 978-3-031-16474-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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