A hybrid multi-population framework for dynamic environments combining online and offline learning

Population based incremental learning algorithms and selection hyper-heuristics are highly adaptive methods which can handle different types of dynamism that may occur while a given problem is being solved. In this study, we present an approach based on a framework hybridizing these approaches to solve dynamic environment problems. A key feature of this hybrid approach is that it also incorporates online learning, which takes place during the search process for a high quality solution to a given instance, mixing it with offline learning which takes place during the training session prior to dealing with the instance. The performance of the approach along with the influence of different heuristic selection methods used within the selection hyper-heuristic is investigated over a range of dynamic environments produced by a well known benchmark generator. The empirical results show that the proposed approach using a particular hyper-heuristic A preliminary version of this study was presented in UKCI 2012: 12th Annual Workshop on Computational Intelligence Gönül Uludağ and Berna Kiraz Institute of Science and Technology Istanbul Technical University Maslak, Istanbul, Turkey 34469 E-mail: [email protected] E-mail: [email protected] A. Şima Etaner-Uyar Department of Computer Engineering Istanbul Technical University Maslak, Istanbul, Turkey 34469 E-mail: [email protected] Ender Özcan School of Computer Science University of Nottingham Nottingham, UK NG8 1BB E-mail: [email protected] outperforms some of the top approaches in literature for dynamic environment problems.