Population-based incremental learning with associative memory for dynamic environments

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dc.contributor.author Yang, Shengxiang en
dc.contributor.author Yao, Xin en
dc.date.accessioned 2012-04-11T10:38:58Z
dc.date.available 2012-04-11T10:38:58Z
dc.date.issued 2008-04
dc.identifier.citation Yang, S. and Yao, X. (2008) Population-based incremental learning with associative memory for dynamic environments. IEEE Transactions on Evolutionary Computation, 12 (5), pp 542-561 en
dc.identifier.issn 1089-778X
dc.identifier.uri http://hdl.handle.net/2086/5895
dc.description.abstract In 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.iso en en
dc.subject associative memory scheme en
dc.subject dynamic optimization problems (DOPs) en
dc.subject immune system-based genetic algorithm (ISGA) en
dc.subject memory-enhanced genetic algorithm en
dc.subject multipopulation scheme en
dc.subject population-based incremental learning (PBIL) en
dc.subject random immigrants en
dc.title Population-based incremental learning with associative memory for dynamic environments en
dc.type Article en
dc.identifier.doi http://dx.doi.org/10.1109/TEVC.2007.913070
dc.researchgroup Centre for Computational Intelligence en
dc.ref2014.selected 1367395509_9911340001952_11_4

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