Grammar-based generation of variable-selection heuristics for constraint satisfaction problems

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Sosa-Ascencio:2015:GPEM,

• author = "Alejandro Sosa-Ascencio and Gabriela Ochoa and Hugo Terashima-Marin and Santiago Enrique Conant-Pablos",
• title = "Grammar-based generation of variable-selection heuristics for constraint satisfaction problems",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2016",
• volume = "17",
• number = "2",
• pages = "119--144",
• month = jun,
• keywords = "genetic algorithms, genetic programming, Constraint satisfaction problems, Hyper-heuristics, Variable ordering heuristics, Grammar-based framework",
• ISSN = "1389-2576",
• DOI = "doi:10.1007/s10710-015-9249-1",
• size = "26 pages",
• 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.",

}

Genetic Programming entries for Alejandro Sosa-Ascencio Gabriela Ochoa Hugo Terashima-Marin Santiago Enrique Conant-Pablos

Citations