Abstract
Selection hyper-heuristic methodologies explore the space of heuristics which in turn explore the space of candidate solutions for solving hard computational problems. This study investigates the performance of approaches based on a framework that hybridizes selection hyper-heuristics and population based incremental learning (PBIL), mixing offline and online learning mechanisms for solving dynamic environment problems. The experimental results over well known benchmark instances show that the approach is generalized enough to provide a good average performance over different types of dynamic environments.
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Uludağ, G., Kiraz, B., Etaner-Uyar, A.Ş., Özcan, E. (2012). A Framework to Hybridize PBIL and a Hyper-heuristic for Dynamic Environments. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32964-7_36
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DOI: https://doi.org/10.1007/978-3-642-32964-7_36
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