Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/5968
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dc.contributor.authorPeng, X-
dc.contributor.authorGao, X-
dc.contributor.authorYang, S-
dc.date.accessioned2011-11-21T11:00:17Z-
dc.date.available2011-11-21T11:00:17Z-
dc.date.issued2011-
dc.identifier.citationSoft Computing, 15(2): 311 - 326, Feb 2011en_US
dc.identifier.issn1432-7643-
dc.identifier.urihttp://www.springerlink.com/content/c6h90x52722056pv/en
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/5968-
dc.descriptionCopyright @ Springer-Verlag 2010.en_US
dc.description.abstractIn estimation of distribution algorithms (EDAs), the joint probability distribution of high-performance solutions is presented by a probability model. This means that the priority search areas of the solution space are characterized by the probability model. From this point of view, an environment identification-based memory management scheme (EI-MMS) is proposed to adapt binary-coded EDAs to solve dynamic optimization problems (DOPs). Within this scheme, the probability models that characterize the search space of the changing environment are stored and retrieved to adapt EDAs according to environmental changes. A diversity loss correction scheme and a boundary correction scheme are combined to counteract the diversity loss during the static evolutionary process of each environment. Experimental results show the validity of the EI-MMS and indicate that the EI-MMS can be applied to any binary-coded EDAs. In comparison with three state-of-the-art algorithms, the univariate marginal distribution algorithm (UMDA) using the EI-MMS performs better when solving three decomposable DOPs. In order to understand the EI-MMS more deeply, the sensitivity analysis of parameters is also carried out in this paper.en_US
dc.description.sponsorshipThis work was supported by the National Nature Science Foundation of China (NSFC) under Grant 60774064, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectEstimation of distribution algorithmen_US
dc.subjectDynamic optimization problemen_US
dc.subjectEnvironment identificationen_US
dc.subjectMemory schemeen_US
dc.subjectDiversity compensationen_US
dc.titleEnvironment identification based memory scheme for estimation of distribution algorithms in dynamic environmentsen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1007/s00500-010-0547-5-
pubs.organisational-data/Brunel-
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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