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Computational Intelligence and Neuroscience
Volume 2018 (2018), Article ID 6103726, 14 pages
https://doi.org/10.1155/2018/6103726
Research Article

Exploring the Impact of Early Decisions in Variable Ordering for Constraint Satisfaction Problems

Tecnológico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501 Sur, Col. Tecnológico, 64849 Monterrey, NL, Mexico

Correspondence should be addressed to José Carlos Ortiz-Bayliss; xm.mseti@ssilyabocj

Received 30 June 2017; Revised 30 November 2017; Accepted 23 January 2018; Published 22 February 2018

Academic Editor: Paolo Gastaldo

Copyright © 2018 José Carlos Ortiz-Bayliss 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

When solving constraint satisfaction problems (CSPs), it is a common practice to rely on heuristics to decide which variable should be instantiated at each stage of the search. But, this ordering influences the search cost. Even so, and to the best of our knowledge, no earlier work has dealt with how first variable orderings affect the overall cost. In this paper, we explore the cost of finding high-quality orderings of variables within constraint satisfaction problems. We also study differences among the orderings produced by some commonly used heuristics and the way bad first decisions affect the search cost. One of the most important findings of this work confirms the paramount importance of first decisions. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. We propose a simple method to improve early decisions of heuristics. By using it, performance of heuristics increases.