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Automatic Generation of Heuristics for Constraint Satisfaction Problems

  • José Carlos Ortiz-BaylissEmail author
  • Jorge Humberto Moreno-Scott
  • Hugo Terashima-Marín
Part of the Studies in Computational Intelligence book series (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.

Keywords

Constraint Satisfaction Heuristics Genetic Algorithms 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • José Carlos Ortiz-Bayliss
    • 1
    Email author
  • Jorge Humberto Moreno-Scott
    • 2
  • Hugo Terashima-Marín
    • 2
  1. 1.Automated Scheduling, Optimisation and Planning (ASAP) School of Computer ScienceUniversity of NottinghamNottinghamUK
  2. 2.Tecnológico de MonterreyMonterreyMexico

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