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dc.contributor.authorSosa-Ascencio, Alejandroen_UK
dc.contributor.authorOchoa, Gabrielaen_UK
dc.contributor.authorTerashima-Marin, Hugoen_UK
dc.contributor.authorConant-Pablos, Santiago Enriqueen_UK
dc.description.abstractWe 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.en_UK
dc.relationSosa-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.
dc.rightsThis 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
dc.subjectConstraint satisfaction problemsen_UK
dc.subjectGenetic programmingen_UK
dc.subjectVariable ordering heuristicsen_UK
dc.subjectGrammar-based frameworken_UK
dc.titleGrammar-based generation of variable-selection heuristics for constraint satisfaction problemsen_UK
dc.typeJournal Articleen_UK
dc.rights.embargoreason[GPEM_GrammarBasedHeuristicsSosaOchoa.pdf] Publisher requires embargo of 12 months after formal publication.en_UK
dc.citation.jtitleGenetic Programming and Evolvable Machinesen_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.contributor.affiliationMonterrey Institute of Technology and Higher Education (Tecnológico de Monterrey)en_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationMonterrey Institute of Technology and Higher Education (Tecnológico de Monterrey)en_UK
dc.contributor.affiliationMonterrey Institute of Technology and Higher Education (Tecnológico de Monterrey)en_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeJournal Article/Reviewen_UK
local.rioxx.authorSosa-Ascencio, Alejandro|en_UK
local.rioxx.authorOchoa, Gabriela|0000-0001-7649-5669en_UK
local.rioxx.authorTerashima-Marin, Hugo|en_UK
local.rioxx.authorConant-Pablos, Santiago Enrique|en_UK
local.rioxx.projectInternal Project|University of Stirling|
Appears in Collections:Computing Science and Mathematics Journal Articles

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