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Automatic Generation of Heuristics for Constraint Satisfaction Problems

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Book cover Nature Inspired Cooperative Strategies for Optimization (NICSO 2013)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 512))

Abstract

The constraint satisfaction problem (CSP) is a generic problem with many applications in different areas of artificial intelligence and operational research. When solving a CSP, the order in which the variables are selected to be instantiated has a tremendous impact in the cost of finding a solution. In this paper we explore a novel type of heuristic that combines different features that describe the current state of the instance to decide which variable to instantiate next. A generational genetic algorithm is used to automatically tune the parameters used by these new heuristics. This paper contributes to the development of new heuristics that can be either very specialized to one class of instances, or general enough to deal with different classes of instances with an acceptable performance.

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Author information

Authors and Affiliations

  1. Automated Scheduling, Optimisation and Planning (ASAP) School of Computer Science, University of Nottingham, Nottingham, UK

    José Carlos Ortiz-Bayliss

  2. Tecnológico de Monterrey, Campus Monterrey, Monterrey, Mexico

    Jorge Humberto Moreno-Scott & Hugo Terashima-Marín

Authors
  1. José Carlos Ortiz-Bayliss
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  2. Jorge Humberto Moreno-Scott
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  3. Hugo Terashima-Marín
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Corresponding author

Correspondence to José Carlos Ortiz-Bayliss .

Editor information

Editors and Affiliations

  1. School of Computer Science, University of Nottingham, Nottingham, United Kingdom

    German Terrazas

  2. School of Computing, University of Kent, Canterbury, Kent, United Kingdom

    Fernando E. B. Otero

  3. Center for Research on ICT, University of Granada, Granada, Spain

    Antonio D. Masegosa

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Ortiz-Bayliss, J.C., Moreno-Scott, J.H., Terashima-Marín, H. (2014). Automatic Generation of Heuristics for Constraint Satisfaction Problems. In: Terrazas, G., Otero, F., Masegosa, A. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2013). Studies in Computational Intelligence, vol 512. Springer, Cham. https://doi.org/10.1007/978-3-319-01692-4_24

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  • DOI: https://doi.org/10.1007/978-3-319-01692-4_24

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-01691-7

  • Online ISBN: 978-3-319-01692-4

  • eBook Packages: EngineeringEngineering (R0)

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