Neural networks to guide the selection of heuristics within constraint satisfaction problems
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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. © 2011 Springer-Verlag Berlin Heidelberg.