dc.contributor.authorNguyen, Quang Huy
dc.contributor.authorOng, Yew Soon
dc.contributor.authorLim, Meng-Hiot
dc.date.accessioned2010-04-30T08:40:00Z
dc.date.available2010-04-30T08:40:00Z
dc.date.copyright2009en_US
dc.date.issued2009
dc.identifier.citationNguyen, Q. H., Ong, Y. S., & Lim, M. H. (2009) Probabilistic Memetic Framework. IEEE Transactions on Evolutionary Computation. 13(3), 604-623.en_US
dc.identifier.issn1089-778Xen_US
dc.identifier.urihttp://hdl.handle.net/10220/6243
dc.description.abstractMemetic algorithms (MAs) represent one of the recent growing areas in evolutionary algorithm (EA) research. The term MAs is now widely used as a synergy of evolutionary or any population-based approach with separate individual learning or local improvement procedures for problem search. Quite often, MAs are also referred to in the literature as Baldwinian EAs, Lamarckian EAs, cultural algorithms, or genetic local searches. In the last decade, MAs have been demonstrated to converge to high-quality solutions more efficiently than their conventional counterparts on a wide range of real-world problems. Despite the success and surge in interests on MAs, many of the successful MAs reported have been crafted to suit problems in very specific domains. Given the restricted theoretical knowledge available in the field of MAs and the limited progress made on formal MA frameworks, we present a novel probabilistic memetic framework that models MAs as a process involving the decision of embracing the separate actions of evolution or individual learning and analyzing the probability of each process in locating the global optimum. Further, the framework balances evolution and individual learning by governing the learning intensity of each individual according to the theoretical upper bound derived while the search progresses. Theoretical and empirical studies on representative benchmark problems commonly used in the literature are presented to demonstrate the characteristics and efficacies of the probabilistic memetic framework. Further, comparisons to recent state-of-the-art evolutionary algorithms, memetic algorithms, and hybrid evolutionary-local search demonstrate that the proposed framework yields robust and improved search performance.en_US
dc.format.extent20 p.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesIEEE transactions on evolutionary computationen_US
dc.rights© 2009 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. This 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. http://www.ieee.org/portal/site This 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.subjectDRNTU::Engineering::Electrical and electronic engineering
dc.titleA probabilistic memetic frameworken_US
dc.typeJournal Article
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1109/TEVC.2008.2009460
dc.description.versionPublished versionen_US


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