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Computational Intelligence and Neuroscience
Volume 2016, Article ID 7349070, 15 pages
http://dx.doi.org/10.1155/2016/7349070
Research Article

Experimental Matching of Instances to Heuristics for Constraint Satisfaction Problems

National School of Engineering and Sciences, Tecnológico de Monterrey, Avenida Eugenio Garza Sada 2501 Sur, Colonia Tecnológico, 64849 Monterrey, NL, Mexico

Received 29 September 2015; Revised 16 December 2015; Accepted 27 December 2015

Academic Editor: Paul C. Kainen

Copyright © 2016 Jorge Humberto Moreno-Scott et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Constraint satisfaction problems are of special interest for the artificial intelligence and operations research community due to their many applications. Although heuristics involved in solving these problems have largely been studied in the past, little is known about the relation between instances and the respective performance of the heuristics used to solve them. This paper focuses on both the exploration of the instance space to identify relations between instances and good performing heuristics and how to use such relations to improve the search. Firstly, the document describes a methodology to explore the instance space of constraint satisfaction problems and evaluate the corresponding performance of six variable ordering heuristics for such instances in order to find regions on the instance space where some heuristics outperform the others. Analyzing such regions favors the understanding of how these heuristics work and contribute to their improvement. Secondly, we use the information gathered from the first stage to predict the most suitable heuristic to use according to the features of the instance currently being solved. This approach proved to be competitive when compared against the heuristics applied in isolation on both randomly generated and structured instances of constraint satisfaction problems.