Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/5987
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dc.contributor.authorYang, S-
dc.contributor.authorYao, X-
dc.date.accessioned2011-11-21T15:57:55Z-
dc.date.available2011-11-21T15:57:55Z-
dc.date.issued2005-
dc.identifier.citationSoft Computing, 9(11): 815 - 834, Nov 2005en_US
dc.identifier.issn1432-7643-
dc.identifier.urihttp://www.springerlink.com/content/u5k11nx786282q16/en
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/5987-
dc.descriptionCopyright @ Springer-Verlag 2005.en_US
dc.description.abstractEvolutionary algorithms have been widely used for stationary optimization problems. However, the environments of real world problems are often dynamic. This seriously challenges traditional evolutionary algorithms. In this paper, the application of population-based incremental learning (PBIL) algorithms, a class of evolutionary algorithms, for dynamic problems is investigated. Inspired by the complementarity mechanism in nature a Dual PBIL is proposed, which operates on two probability vectors that are dual to each other with respect to the central point in the genotype space. A diversity maintaining technique of combining the central probability vector into PBIL is also proposed to improve PBILs adaptability in dynamic environments. In this paper, a new dynamic problem generator that can create required dynamics from any binary-encoded stationary problem is also formalized. Using this generator, a series of dynamic problems were systematically constructed from several benchmark stationary problems and an experimental study was carried out to compare the performance of several PBIL algorithms and two variants of standard genetic algorithm. Based on the experimental results, we carried out algorithm performance analysis regarding the weakness and strength of studied PBIL algorithms and identified several potential improvements to PBIL for dynamic optimization problems.en_US
dc.description.sponsorshipThis work was was supported by UK EPSRC under Grant GR/S79718/01.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectPopulation-based incremental learningen_US
dc.subjectDynamic optimization problemen_US
dc.subjectDual population-based incremental learningen_US
dc.subjectGenetic algorithmen_US
dc.subjectCentral probability vectoren_US
dc.subjectExclusive-or operatoren_US
dc.titleExperimental study on population-based incremental learning algorithms for dynamic optimization problemsen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1007/s00500-004-0422-3-
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-
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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