Abstract
The present study proposes a new selection hyper-heuristic providing several adaptive features to cope with the requirements of managing different heuristic sets. The approach suggested provides an intelligent way of selecting heuristics, determines effective heuristic pairs and adapts the parameters of certain heuristics online. In addition, an adaptive list-based threshold accepting mechanism has been developed. It enables deciding whether to accept or not the solutions generated by the selected heuristics. The resulting approach won the first Cross Domain Heuristic Search Challenge against 19 high-level algorithms.
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Mısır, M., Verbeeck, K., De Causmaecker, P., Vanden Berghe, G. (2012). An Intelligent Hyper-Heuristic Framework for CHeSC 2011. In: Hamadi, Y., Schoenauer, M. (eds) Learning and Intelligent Optimization. LION 2012. Lecture Notes in Computer Science, vol 7219. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34413-8_45
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DOI: https://doi.org/10.1007/978-3-642-34413-8_45
Publisher Name: Springer, Berlin, Heidelberg
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