NDLI: Coevolutionary genetic algorithm for variable ordering in CSPs

Content Provider	IEEE Xplore Digital Library
Author	Karim, M.R. Mouhoub, M.
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci., Univ. of Regina, Regina, SK, Canada (Karim, M.R.; Mouhoub, M.)
Abstract	A Constraint Satisfaction Problem (CSP) is a framework used for modeling and solving constrained problems. Tree-search algorithms like backtracking try to construct a solution to a CSP by selecting the variables of the problem one after another. The order in which these algorithm select the variables potentially have significant impact on the search performance. Various heuristics have been proposed for choosing good variable ordering. Many powerful variable ordering heuristics weigh the constraints first and then utilize the weights for selecting good order of the variables. Constraint weighting are basically employed to identify global bottlenecks in a CSP. In this paper, we propose a new approach for learning weights for the constraints using competitive coevolutionary Genetic Algorithm (GA). Weights learned by the coevolutionary GA later help to make better choices for the first few variables in a search. In the competitive coevolutionary GA, constraints and candidate solutions for a CSP evolve together through an inverse fitness interaction process. We have conducted experiments on several random, quasi-random and patterned instances to measure the efficiency of the proposed approach. The results and analysis show that the proposed approach is good at learning weights to distinguish the hard constraints for quasi-random instances and forced satisfiable random instances generated with the Model RB. For other type of instances, RNDI (RaNDom Information gathering) still seems to be the best approach as our experiments show.
Starting Page	2716
Ending Page	2723
File Size	280699
Page Count	8
File Format	PDF
e-ISBN	9781479914883
DOI	10.1109/CEC.2014.6900262
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic algorithms Sociology Statistics History Systematics Standards Genetics Genetic Algorithm Constraint Satisfaction Problem Variable Ordering Competitive Coevolution
Content Type	Text
Resource Type	Article

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