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dc.contributor.authorYang, Shengxiangen
dc.contributor.authorYao, Xinen
dc.date.accessioned2013-05-17T10:28:12Z
dc.date.available2013-05-17T10:28:12Z
dc.date.issued2008
dc.identifier.citationYang, S. and Yao, X. (2008) Population-based incremental learning with associative memory for dynamic environments. IEEE Transactions on Evolutionary Computation, 12(5), October 2008, pp. 542-561.en
dc.identifier.issn1089-778X
dc.identifier.urihttp://hdl.handle.net/2086/8592
dc.description.abstractIn recent years, interest in studying evolutionary algorithms (EAs) for dynamic optimization problems (DOPs) has grown due to its importance in real-world applications. Several approaches, such as the memory and multiple population schemes, have been developed for EAs to address dynamic problems. This paper investigates the application of the memory scheme for population-based incremental learning (PBIL) algorithms, a class of EAs, for DOPs. A PBIL-specific associative memory scheme, which stores best solutions as well as corresponding environmental information in the memory, is investigated to improve its adaptability in dynamic environments. In this paper, the interactions between the memory scheme and random immigrants, multipopulation, and restart schemes for PBILs in dynamic environments are investigated. In order to better test the performance of memory schemes for PBILs and other EAs in dynamic environments, this paper also proposes a dynamic environment generator that can systematically generate dynamic environments of different difficulty with respect to memory schemes. Using this generator, a series of dynamic environments are generated and experiments are carried out to compare the performance of investigated algorithms. The experimental results show that the proposed memory scheme is efficient for PBILs in dynamic environments and also indicate that different interactions exist between the memory scheme and random immigrants, multipopulation schemes for PBILs in different dynamic environments.en
dc.language.isoenen
dc.publisherIEEEen
dc.subjectAssociative memory schemeen
dc.subjectDynamic optimization problems (DOPs)en
dc.subjectImmune system-based genetic algorithm (ISGA)en
dc.subjectMemory-enhanced genetic algorithmen
dc.subjectMultipopulation schemeen
dc.subjectPopulation-based incremental learning (PBIL)en
dc.subjectRandom immigrantsen
dc.titlePopulation-based incremental learning with associative memory for dynamic environments.en
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1109/TEVC.2007.913070
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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