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
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
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
Learning vector quantization for variable ordering in constraint satisfaction problems
Pattern Recognition Letters
Exploring the solution of course timetabling problems through heuristic segmentation
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
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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. There are instances of CSP that are harder to be solved than others, this due to the constraint and the conflict density [4]. The testebed is composed of hard problems randomly generated by an algorithm proposed by Prosser [18].