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dc.contributor.authorYang, Shengxiangen
dc.contributor.authorRichter, Hendriken
dc.identifier.citationYang, L. and Richter, H. (2009) Hyper-learning for population-based incremental learning in dynamic environments . In: Proceedings of the 2009 IEEE Congress on Evolutionary Computation, Trondheim, 2009. New York: IEEE, pp. 682-689.en
dc.description.abstractThe population-based incremental learning (PBIL) algorithm is a combination of evolutionary optimization and competitive learning. Recently, the PBIL algorithm has been applied for dynamic optimization problems. This paper investigates the effect of the learning rate, which is a key parameter of PBIL, on the performance of PBIL in dynamic environments. A hyper-learning scheme is proposed for PBIL, where the learning rate is temporarily raised whenever the environment changes. The hyper-learning scheme can be combined with other approaches, e.g., the restart and hypermutation schemes, for PBIL in dynamic environments. Based on a series of dynamic test problems, experiments are carried out to investigate the effect of different learning rates and the proposed hyper-learning scheme in combination with restart and hypermutation schemes on the performance of PBIL. The experimental results show that the learning rate has a significant impact on the performance of the PBIL algorithm in dynamic environments and that the effect of the proposed hyper-learning scheme depends on the environmental dynamics and other schemes combined in the PBIL algorithm.en
dc.subjectEvolutionary computationen
dc.subjectLearning (artificial intelligence)en
dc.subjectDynamic optimization problemsen
dc.subjectHyper-learning schemeen
dc.subjectHypermutation schemesen
dc.subjectPopulation-based incremental learning algorithmen
dc.titleHyper-learning for population-based incremental learning in dynamic environments.en
dc.researchgroupCentre for Computational Intelligenceen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en

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