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Memetic gradient search

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dc.contributor.author Li, Boyang
dc.contributor.author Ong, Yew Soon
dc.contributor.author Le, Minh Nghia
dc.contributor.author Goh, Chi Keong
dc.date.accessioned 2009-03-09T03:42:54Z
dc.date.available 2009-03-09T03:42:54Z
dc.date.copyright 2008
dc.date.issued 2009-03-09T03:42:54Z
dc.identifier.citation Li, B., Ong, Y. S., Le, M. N., & Goh, C. K. (2008). Memetic gradient search. IEEE Congress on Evolutionary Computation (2008:Hong Kong)
dc.identifier.uri http://hdl.handle.net/10220/4506
dc.description.abstract This paper reviews the different gradient-based schemes and the sources of gradient, their availability, precision and computational complexity, and explores the benefits of using gradient information within a memetic framework in the context of continuous parameter optimization, which is labeled here as Memetic Gradient Search. In particular, we considered a quasi-Newton method with analytical gradient and finite differencing, as well as simultaneous perturbation stochastic approximation, used as the local searches. Empirical study on the impact of using gradient information showed that Memetic Gradient Search outperformed the traditional GA and analytical, precise gradient brings considerable benefit to gradient-based local search (LS) schemes. Though gradient-based searches can sometimes get trapped in local optima, memetic gradient searches were still able to converge faster than the conventional GA.
dc.format.extent 8 p.
dc.language.iso en
dc.rights © 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.
dc.title Memetic gradient search
dc.type Conference Paper
dc.contributor.conference IEEE Congress on Evolutionary Computation (2008 : Hong Kong)
dc.contributor.research Emerging Research Lab
dc.contributor.school School of Computer Engineering
dc.description.version Accepted version
dc.contributor.organization Nanyang Technological University

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