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Algorithm Selection on Adaptive Operator Selection: A Case Study on Genetic Algorithms

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Learning and Intelligent Optimization (LION 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12931))

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Abstract

The present study applies Algorithm Selection (AS) to Adaptive Operator Selection (AOS) for further improving the performance of the AOS methods. AOS aims at delivering high performance in solving a given problem through combining the strengths of multiple operators. Although the AOS methods are expected to outperform running each operator separately, there is no one AOS method can consistently perform the best. Thus, there is still room for improvement which can be provided by using the best AOS method for each problem instance being solved. For this purpose, the AS problem on AOS is investigated. The underlying AOS methods are applied to choose the crossover operator for a Genetic Algorithm (GA). The Quadratic Assignment Problem (QAP) is used as the target problem domain. For carrying out AS, a suite of simple and easy-to-calculate features characterizing the QAP instances is introduced. The corresponding empirical analysis revealed that AS offers improved performance and robustness by utilizing the strenghts of different AOS approaches.

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Notes

  1. 1.

    a hyper-heuristic bibliography: https://mustafamisir.github.io/hh.html.

  2. 2.

    http://www.yisongyue.com/courses/cs159/lectures/rl-notes.pdf.

  3. 3.

    http://www.satcompetition.org/.

  4. 4.

    http://argo.matf.bg.ac.rs/downloads/software/ArgoSmart.zip.

  5. 5.

    http://argo.matf.bg.ac.rs/downloads/software/ArgoSmartkNN.zip.

  6. 6.

    https://sites.google.com/site/yurimalitsky/downloads/SNNAP_ver1.5.zip.

  7. 7.

    https://sites.google.com/site/yurimalitsky/downloads/3S-2011.tar.

  8. 8.

    http://homepages.laas.fr/ehebrard/cphydra.html.

  9. 9.

    https://github.com/CP-Unibo/sunny-as.

  10. 10.

    http://www.cril.univ-artois.fr/~roussel/ppfolio/.

  11. 11.

    http://www.cs.ubc.ca/labs/beta/Projects/Hydra/ – unrelated to the aforementioned CPHydra.

  12. 12.

    http://research.larc.smu.edu.sg/adviser/.

  13. 13.

    http://research.larc.smu.edu.sg/adviserplus/.

  14. 14.

    http://aslib.net.

  15. 15.

    http://anjos.mgi.polymtl.ca/qaplib/.

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Acknowledgement

This study was supported by the 2232 Reintegration Grant from Scientific and Technological Research Council of Turkey (TUBITAK) under Project 119C013.

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Authors and Affiliations

  1. Istinye University, Istanbul, Turkey

    Mustafa Mısır

  2. Duke Kunshan University, Kunshan, China

    Mustafa Mısır

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  1. Mustafa Mısır
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    Dimitris E. Simos

  2. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  3. Wilfrid Laurier University, Waterloo, ON, Canada

    Ilias S. Kotsireas

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Mısır, M. (2021). Algorithm Selection on Adaptive Operator Selection: A Case Study on Genetic Algorithms. In: Simos, D.E., Pardalos, P.M., Kotsireas, I.S. (eds) Learning and Intelligent Optimization. LION 2021. Lecture Notes in Computer Science(), vol 12931. Springer, Cham. https://doi.org/10.1007/978-3-030-92121-7_20

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