Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
Experimental evaluation of preprocessing algorithms for constraint satisfaction problems
Artificial Intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Intelligence through simulated evolution: forty years of evolutionary programming
Intelligence through simulated evolution: forty years of evolutionary programming
A genetic local search algorithm for random binary constraint satisfaction problems
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
New methods to color the vertices of a graph
Communications of the ACM
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A GA-based method to produce generalized hyper-heuristics for the 2D-regular cutting stock problem
Proceedings of the 8th annual conference on Genetic and evolutionary computation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Comparing evolutionary algorithms on binary constraint satisfaction problems
IEEE Transactions on Evolutionary Computation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
Variable and value ordering decision matrix hyper-heuristics: a local improvement approach
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Improving the performance of vector hyper-heuristics through local search
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Learning vector quantization for variable ordering in constraint satisfaction problems
Pattern Recognition Letters
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The idea behind hyper-heuristics is to discover some combination of straightforward heuristics to solve a wide range of problems. To be worthwhile, such combination should outperform the single heuristics. This paper presents a GA-based method that produces general hyper-heuristics for the dynamic variable ordering within Constraint Satisfaction Problems. The GA uses a variable-length representation, which evolves combinations of condition-action rules producing hyper-heuristics after going through a learning process which includes training and testing phases. Such hyper-heuristics, when tested with a large set of benchmark problems, produce encouraging results for most of the cases. The testebed is composed of problems randomly generated using an algorithm proposed by Prosser.