Variable and value ordering decision matrix hyper-heuristics: a local improvement approach

Authors:
José Carlos Ortiz-Bayliss;Hugo Terashima-Marín;Ender Özcan;Andrew J. Parkes;Santiago Enrique Conant-Pablos
Affiliations:
Tecnológico de Monterrey, Monterrey, Mexico;Tecnológico de Monterrey, Monterrey, Mexico;University of Nottingham, Nottingham, United Kingdom;University of Nottingham, Nottingham, United Kingdom;Tecnológico de Monterrey, Monterrey, Mexico
Venue:
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Year:
2011

Citing 8
Cited 1

Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems

Artificial Intelligence - Special volume on constraint-based reasoning
New methods to color the vertices of a graph

Communications of the ACM
Automated discovery of local search heuristics for satisfiability testing

Evolutionary Computation
Hyper-heuristics for the dynamic variable ordering in constraint satisfaction problems

Proceedings of the 10th annual conference on Genetic and evolutionary computation
Learning and using hyper-heuristics for variable and value ordering in constraint satisfaction problems

Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
High performance ATP systems by combining several AI methods

IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Where the really hard problems are

IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
On the idea of evolving decision matrix hyper-heuristics for solving constraint satisfaction problems

Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

Learning vector quantization for variable ordering in constraint satisfaction problems

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.