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Lifelong Learning Selection Hyper-heuristics for Constraint Satisfaction Problems

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Advances in Artificial Intelligence and Soft Computing (MICAI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9413))

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Abstract

Selection hyper-heuristics are methods that manage the use of different heuristics and recommend one of them that is suitable for the current problem space under exploration. In this paper we describe a hyper-heuristic model for constraint satisfaction that is inspired in the idea of a lifelong learning process that allows the hyper-heuristic to continually improve the quality of its decisions by incorporating information from every instance it solves. The learning takes place in a transparent way because the learning process is executed in parallel in a different thread than the one that deals with the user’s requests. We tested the model on various constraint satisfaction problem instances and obtained promising results, specially when tested on unseen instances from different classes.

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Acknowledgments

This research was supported in part by ITESM Research Group with Strategic Focus in Intelligent Systems and CONACyT Basic Science Project under grant 241461.

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Authors and Affiliations

  1. Tecnológico de Monterrey National School of Engineering and Sciences, Av. Eugenio Garza Sada 2501 Sur Col. Tecnológico, 64849, Monterrey, Nuevo Leon, Mexico

    José Carlos Ortiz-Bayliss, Hugo Terashima-Marín & Santiago Enrique Conant-Pablos

Authors
  1. José Carlos Ortiz-Bayliss
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  2. Hugo Terashima-Marín
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  3. Santiago Enrique Conant-Pablos
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Corresponding author

Correspondence to José Carlos Ortiz-Bayliss .

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Editors and Affiliations

  1. Instituto Politécnico Nacional, Centro de Investigación en Computación, Mexico City, Mexico

    Grigori Sidorov

  2. Facultad de ciencias, Universidad Autónoma Nacional, México, Distrito Federal, Mexico

    Sofía N. Galicia-Haro

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Ortiz-Bayliss, J.C., Terashima-Marín, H., Conant-Pablos, S.E. (2015). Lifelong Learning Selection Hyper-heuristics for Constraint Satisfaction Problems. In: Sidorov, G., Galicia-Haro, S. (eds) Advances in Artificial Intelligence and Soft Computing. MICAI 2015. Lecture Notes in Computer Science(), vol 9413. Springer, Cham. https://doi.org/10.1007/978-3-319-27060-9_15

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  • DOI: https://doi.org/10.1007/978-3-319-27060-9_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27059-3

  • Online ISBN: 978-3-319-27060-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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