Lifelong learning selection hyper-heuristics for constraint satisfaction problems
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© Springer International Publishing Switzerland 2015.Selection hyper-heuristics are methods that manage the use of different heuristics and recommend one of them that is suitable for the current problem space under exploration. In this paper we describe a hyper-heuristic model for constraint satisfaction that is inspired in the idea of a lifelong learning process that allows the hyper-heuristic to continually improve the quality of its decisions by incorporating information from every instance it solves. The learning takes place in a transparent way because the learning process is executed in parallel in a different thread than the one that deals with the user's requests. We tested the model on various constraint satisfaction problem instances and obtained promising results, specially when tested on unseen instances from different classes.