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dc.contributor.authorYang, S-
dc.contributor.authorRichter, H-
dc.identifier.citation2009 IEEE Congress on Evolutionary Computation, Trondheim: 682 - 689, 18 - 21 May 2009en_US
dc.descriptionThis article is posted here here with permission from IEEE - Copyright @ 2009 IEEEen_US
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_US
dc.description.sponsorshipThe work by Shengxiang Yang was supported by the Engineering and Physical Sciences Research Council (EPSRC) of the United Kingdom under Grant EP/E060722/1.en_US
dc.subjectComputer scienceen_US
dc.subjectConstraint optimizationen_US
dc.subjectEvolutionary computationen_US
dc.subjectHeuristic algorithmsen_US
dc.subjectPerformance analysisen_US
dc.titleHyper-learning for population-based incremental learning in dynamic environmentsen_US
dc.typeConference Paperen_US
pubs.organisational-data/Brunel/Brunel (Active)-
pubs.organisational-data/Brunel/Brunel (Active)/School of Info. Systems, Comp & Maths-
pubs.organisational-data/Brunel/Research Centres (RG)-
pubs.organisational-data/Brunel/Research Centres (RG)/CIKM-
pubs.organisational-data/Brunel/School of Information Systems, Computing and Mathematics (RG)-
pubs.organisational-data/Brunel/School of Information Systems, Computing and Mathematics (RG)/CIKM-
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Computer Science
Dept of Computer Science Research Papers

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