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
Constraint-based reasoning
Backtrack programming techniques
Communications of the ACM
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
A comprehensive analysis of hyper-heuristics
Intelligent Data Analysis
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
Search rearrangement backtracking and polynomial average time
Artificial Intelligence
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
Learning vector quantization for variable ordering in constraint satisfaction problems
Pattern Recognition Letters
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Hyper-heuristics are methodologies used to choose from a set of heuristics and decide which one to apply given some properties of the current instance. When solving a Constraint Satisfaction Problem, the order in which the variables are selected to be instantiated has implications in the complexity of the search. We propose a neural network hyper-heuristic approach for variable ordering within Constraint Satisfaction Problems. The first step in our approach requires to generate a pattern that maps any given instance, expressed in terms of constraint density and tightness, to one adequate heuristic. That pattern is later used to train various neural networks which represent hyper-heuristics. The results suggest that neural networks generated through this methodology represent a feasible alternative to code hyper-heuristic which exploit the strengths of the heuristics to minimise the cost of finding a solution.