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dc.contributor.authorMavrovouniotis, Michalisen
dc.contributor.authorYang, Shengxiangen
dc.date.accessioned2016-04-04T15:30:51Z
dc.date.available2016-04-04T15:30:51Z
dc.date.issued2015-12
dc.identifier.citationMavrovouniotis, M. and Yang, S. (2015) Population-based incremental learning with immigrants schemes in changing environments. 2015 IEEE Symposium Series on Computational Intelligence, pp. 1444-1451en
dc.identifier.isbn9781479975600
dc.identifier.urihttp://hdl.handle.net/2086/11815
dc.description.abstractThe population-based incremental learning (PBIL) algorithm is a combination of evolutionary optimization and competitive learning. PBIL has been successfully applied to dynamic optimization problems (DOPs). It is well known that maintaining the population diversity is important for PBIL to adapt well to dynamic changes. However, PBIL faces a serious challenge when applied to DOPs because at early stages of the optimization process the population diversity is decreased significantly. It has been shown that random immigrants can increase the diversity level maintained by PBIL algorithms and enhance their performance on some DOPs. In this paper, we integrate elitism-based and hybrid immigrants into PBIL to address slightly and severely changing DOPs. Based on a series of dynamic test problems, experiments are conducted to investigate the effect of immigrants schemes on the performance of PBIL. The experimental results show that the integration of elitism-based and hybrid immigrants with PBIL always improves the performance when compared with a standard PBIL on different DOPs. Finally, the proposed PBILs are compared with other peer algorithms and show competitive performance.en
dc.language.isoen_USen
dc.publisherIEEEen
dc.subjectPopulation-based incremental learningen
dc.subjectimmigrants schemesen
dc.subjectdynamic optimization problemsen
dc.titlePopulation-based incremental learning with immigrants schemes in changing environmentsen
dc.typeConferenceen
dc.identifier.doihttp://dx.doi.org/10.1109/SSCI.2015.205
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.explorer.multimediaNoen
dc.funderEPSRC (Engineering and Physical Sciences Research Council)en
dc.projectidEP/K001310/1en
dc.cclicenceN/Aen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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