Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/292
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorKu, KWC-
dc.creatorMak, MW-
dc.creatorSiu, WC-
dc.date.accessioned2014-12-11T08:28:24Z-
dc.date.available2014-12-11T08:28:24Z-
dc.identifier.issn1045-9227-
dc.identifier.urihttp://hdl.handle.net/10397/292-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_US
dc.rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en_US
dc.subjectBaldwin effecten_US
dc.subjectGenetic algorithmsen_US
dc.subjectLamarckian learningen_US
dc.subjectReal-time recurrent learningen_US
dc.subjectRecurrent neural networksen_US
dc.titleAdding learning to cellular genetic algorithms for training recurrent neural networksen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage239-
dc.identifier.epage252-
dc.identifier.volume10-
dc.identifier.issue2-
dc.identifier.doi10.1109/72.750546-
dcterms.abstractThis paper proposes a hybrid optimization algorithm which combines the efforts of local search (individual learning) and cellular genetic algorithms (GA's) for training recurrent neural networks (RNN's). Each weight of an RNN is encoded as a floating point number, and a concatenation of the numbers forms a chromosome. Reproduction takes place locally in a square grid with each grid point representing a chromosome. Two approaches, Lamarckian and Baldwinian mechanisms, for combining cellular GA's and learning have been compared. Different hill-climbing algorithms are incorporated into the cellular GA's as learning methods. These include the real-time recurrent learning (RTRL) and its simplified versions, and the delta rule. The RTRL algorithm has been successively simplified by freezing some of the weights to form simplified versions. The delta rule, which is the simplest form of learning, has been implemented by considering the RNN's as feedforward networks during learning. The hybrid algorithms are used to train the RNN's to solve a long-term dependency problem. The results show that Baldwinian learning is inefficient in assisting the cellular GA. It is conjectured that the more difficult it is for genetic operations to produce the genotypic changes that match the phenotypic changes due to learning, the poorer is the convergence of Baldwinian learning. Most of the combinations using the Lamarckian mechanism show an improvement in reducing the number of generations required for an optimum network; however, only a few can reduce the actual time taken. Embedding the delta rule in the cellular GA's has been found to be the fastest method. It is also concluded that learning should not be too extensive if the hybrid algorithm is to be benefit from learning.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on neural networks, Mar. 1999, v. 10, no. 2, p. 239-252-
dcterms.isPartOfIEEE transactions on neural networks-
dcterms.issued1999-03-
dc.identifier.isiWOS:000079154400003-
dc.identifier.scopus2-s2.0-0033101201-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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