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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Grammar-based generation of variable-selection heuristics for constraint satisfaction problems
Author(s): Sosa-Ascencio, Alejandro
Ochoa, Gabriela
Terashima-Marin, Hugo
Conant-Pablos, Santiago Enrique
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Keywords: Constraint satisfaction problems
Genetic programming
Variable ordering heuristics
Grammar-based framework
Issue Date: Jun-2016
Date Deposited: 22-Aug-2016
Citation: Sosa-Ascencio A, Ochoa G, Terashima-Marin H & Conant-Pablos SE (2016) Grammar-based generation of variable-selection heuristics for constraint satisfaction problems. Genetic Programming and Evolvable Machines, 17 (2), pp. 119--144.
Abstract: We propose a grammar-based genetic programming framework that generates variable-selection heuristics for solving constraint satisfaction problems. This approach can be considered as a generation hyper-heuristic. A grammar to express heuristics is extracted from successful human-designed variable-selection heuristics. The search is performed on the derivation sequences of this grammar using a strongly typed genetic programming framework. The approach brings two innovations to grammar-based hyper-heuristics in this domain: the incorporation of if-then-else rules to the function set, and the implementation of overloaded functions capable of handling different input dimensionality. Moreover, the heuristic search space is explored using not only evolutionary search, but also two alternative simpler strategies, namely, iterated local search and parallel hill climbing. We tested our approach on synthetic and real-world instances. The newly generated heuristics have an improved performance when compared against human-designed heuristics. Our results suggest that the constrained search space imposed by the proposed grammar is the main factor in the generation of good heuristics. However, to generate more general heuristics, the composition of the training set and the search methodology played an important role. We found that increasing the variability of the training set improved the generality of the evolved heuristics, and the evolutionary search strategy produced slightly better results.
DOI Link: 10.1007/s10710-015-9249-1
Rights: This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. Publisher policy allows this work to be made available in this repository. Published in Genetic Programming and Evolvable Machines June 2016, Volume 17, Issue 2, pp 119–144. The final publication is available at Springer via

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