Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Generating hard satisfiability problems
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
Backtrack programming techniques
Communications of the ACM
Self-Organizing Maps
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
An information-based neural approach to generic constraint satisfaction
Artificial Intelligence
Generating Satisfiable Problem Instances
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
An Information-Based Neural Approach to Constraint Satisfaction
Neural Computation
Random constraint satisfaction: Easy generation of hard (satisfiable) instances
Artificial Intelligence
A comprehensive analysis of hyper-heuristics
Intelligent Data Analysis
Hyper-heuristics for the dynamic variable ordering in constraint satisfaction problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Using Hyper-heuristics for the Dynamic Variable Ordering in Binary Constraint Satisfaction Problems
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Exact phase transitions in random constraint satisfaction problems
Journal of Artificial Intelligence Research
High performance ATP systems by combining several AI methods
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Sparse constraint graphs and exceptionally hard problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Search rearrangement backtracking and polynomial average time
Artificial Intelligence
Learning vector quantization with adaptive prototype addition and removal
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
An experimental study on hyper-heuristics and exam timetabling
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
Solving large-scale constraint satisfaction and scheduling problems using a heuristic repair method
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Using deep structure to locate hard problems
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Lagrange neural network for solving CSP which includes linear inequality constraints
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Neural networks to guide the selection of heuristics within constraint satisfaction problems
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
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
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A constraint satisfaction problem (CSP) is a generic problem with many applications in different areas of artificial intelligence and operational research. During the search for a solution, the order in which the variables are selected to be instantiated has a tremendous impact in the complexity of the search. Many heuristics exist for ordering these variables, but they are specialized for some types of instances. Hyper-heuristics are methodologies used to choose from a set of heuristics and decide which one to apply given some properties of the instance at hand. In this research, we propose a new type of hyper-heuristic based on a learning vector quantization (LVQ) neural network for variable ordering within CSP. The first part of the investigation describes a methodology to generate LVQ neural networks that are able to map some properties of the instances to one suitable variable ordering heuristic. Later, the networks are created according to such methodology and tested on random and real instances proving that they are a feasible method to improve the performance of search on CSP when compared to single heuristics applied in isolation.