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Variable and Value Ordering Decision Matrix Hyper-heuristics: A Local Improvement Approach

  • José Carlos Ortiz-Bayliss
  • Hugo Terashima-Marín
  • Ender Özcan
  • Andrew J. Parkes
  • Santiago Enrique Conant-Pablos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7094)

Abstract

Constraint Satisfaction Problems (CSP) represent an important topic of study because of their 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 and the order of the corresponding values to be tried affect the complexity of the search. Hyper-heuristics are flexible methods that provide generality when solving different problems and, within CSP, they can be used to determine the next variable and value to try. They select from a set of low-level heuristics and decide which one to apply at each decision point according to the problem state. This study explores a hyper-heuristic model for variable and value ordering within CSP based on a decision matrix hyper-heuristic that is constructed by going into a local improvement method that changes small portions of the matrix. The results suggest that the approach is able to combine the strengths of different low-level heuristics to perform well on a wide range of instances and compensate for their weaknesses on specific instances.

Keywords

Constraint Satisfaction Hyper-heuristics Variable and Value Ordering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • José Carlos Ortiz-Bayliss
    • 1
  • Hugo Terashima-Marín
    • 1
  • Ender Özcan
    • 2
  • Andrew J. Parkes
    • 2
  • Santiago Enrique Conant-Pablos
    • 1
  1. 1.Tecnológico de MonterreyMonterreyMexico
  2. 2.University of NottinghamNottinghamUnited Kingdom

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