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\iteman{io-port 05987404}
\itemau{Ortiz-Bayliss, Jos\'e Carlos; Terashima-Mar{\'\i}n, Hugo; \"Ozcan, Ender; Parkes, Andrew J.; Conant-Pablos, Santiago Enrique}
\itemti{Variable and value ordering decision matrix hyper-heuristics: a local improvement approach.}
\itemso{Batyrshin, Ildar (ed.) et al., Advances in artificial intelligence. 10th Mexican international conference on artificial intelligence, MICAI 2011, Puebla, Mexico, November 26--December 4, 2011. Proceedings, Part I. Berlin: Springer (ISBN 978-3-642-25323-2/pbk). Lecture Notes in Computer Science 7094. Lecture Notes in Artificial Intelligence, 125-136 (2011).}
\itemab
Summary: Constraint Satisfaction Problems (CSP) represent an important topic of study because of their many applications in different areas of artificial intelligence and operational research. When solving a CSP, the order in which the variables are selected to be instantiated and the order of the corresponding values to be tried affect the complexity of the search. Hyper-heuristics are flexible methods that provide generality when solving different problems and, within CSP, they can be used to determine the next variable and value to try. They select from a set of low-level heuristics and decide which one to apply at each decision point according to the problem state. This study explores a hyper-heuristic model for variable and value ordering within CSP based on a decision matrix hyper-heuristic that is constructed by going into a local improvement method that changes small portions of the matrix. The results suggest that the approach is able to combine the strengths of different low-level heuristics to perform well on a wide range of instances and compensate for their weaknesses on specific instances.
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\itemut{constraint satisfaction; hyper-heuristics; variable and value ordering}
\itemli{doi:10.1007/978-3-642-25324-9\_11}
\end