Show simple item record

dc.contributor.authorWang, Hongfengen
dc.contributor.authorYang, Shengxiangen
dc.contributor.authorIp, W. H.en
dc.contributor.authorWang, Dingweien
dc.date.accessioned2013-05-17T10:06:41Z
dc.date.available2013-05-17T10:06:41Z
dc.date.issued2009
dc.identifier.citationWang, H. et al. (2009) Adaptive primal-dual genetic algorithms in dynamic environments. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 39(6), December 2009, pp. 1348-1361.en
dc.identifier.issn1083-4419
dc.identifier.urihttp://hdl.handle.net/2086/8582
dc.description.abstractRecently, there has been an increasing interest in applying genetic algorithms (GAs) in dynamic environments. Inspired by the complementary and dominance mechanisms in nature, a primal-dual GA (PDGA) has been proposed for dynamic optimization problems (DOPs). In this paper, an important operator in PDGA, i.e., the primal-dual mapping (PDM) scheme, is further investigated to improve the robustness and adaptability of PDGA in dynamic environments. In the improved scheme, two different probability-based PDM operators, where the mapping probability of each allele in the chromosome string is calculated through the statistical information of the distribution of alleles in the corresponding gene locus over the population, are effectively combined according to an adaptive Lamarckian learning mechanism. In addition, an adaptive dominant replacement scheme, which can probabilistically accept inferior chromosomes, is also introduced into the proposed algorithm to enhance the diversity level of the population. Experimental results on a series of dynamic problems generated from several stationary benchmark problems show that the proposed algorithm is a good optimizer for DOPs.en
dc.language.isoenen
dc.publisherIEEEen
dc.subjectAdaptive dominant replacement schemeen
dc.subjectLamarckian learningen
dc.subjectDynamic optimization problem (DOP)en
dc.subjectGenetic algorithm (GA)en
dc.subjectPrimal–dual mapping (PDM)en
dc.titleAdaptive primal–dual genetic algorithms in dynamic environments.en
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1109/TSMCB.2009.2015281
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record