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dc.contributor.authorSharma, SK
dc.contributor.authorSutton, R
dc.contributor.authorIrwin, G
dc.date.accessioned2017-02-15T16:35:01Z
dc.date.available2017-02-15T16:35:01Z
dc.date.issued2012-04-01
dc.identifier.issn0952-1976
dc.identifier.issn1873-6769
dc.identifier.urihttp://hdl.handle.net/10026.1/8491
dc.descriptionThe technique reported in this paper is an extension of the new technique by the main author (Sharma S) of fuzzy coding reported in IEEE transaction on Evolutionary Computation in 2003 and uses a novel combination of fuzzy logic and genetic algorithms technologies to significantly improve the speed and diversity of the optimum search. This technique is now being used by OWEL to find the optimal operating conditions for the 350kW marine demonstrator of their wave energy converter as part of a Technology Strategy Board funded project (Email contact: Ned Minns ned.minns@itpower.co.uk).
dc.description.abstract

A technique for automatic exploration of the genetic search region through fuzzy coding (Sharma and Irwin, 2003) has been proposed. Fuzzy coding (FC) provides the value of a variable on the basis of the optimum number of selected fuzzy sets and their effectiveness in terms of degree-of-membership. It is an indirect encoding method and has been shown to perform better than other conventional binary, Gray and floating-point encoding methods. However, the static range of the membership functions is a major problem in fuzzy coding, resulting in longer times to arrive at an optimum solution in large or complicated search spaces. This paper proposes a new algorithm, called fuzzy coding with a dynamic range (FCDR), which dynamically allocates the range of the variables to evolve an effective search region, thereby achieving faster convergence. Results are presented for two benchmark optimisation problems, and also for a case study involving neural identification of a highly non-linear pH neutralisation process from experimental data. It is shown that dynamic exploration of the genetic search region is effective for parameter optimisation in problems where the search space is complicated. © 2011 Published by Elsevier Ltd. All rights reserved.

dc.format.extent443-456
dc.languageen
dc.language.isoen
dc.publisherElsevier BV
dc.subjectGenetic algorithm
dc.subjectGlobal optimisation
dc.subjectDynamic search
dc.subjectRandom search
dc.titleDynamic Evolution of the Genetic Search Region through Fuzzy Coding
dc.typejournal-article
dc.typeArticle
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000301764500001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue3
plymouth.volume25
plymouth.publisher-urlhttp://dx.doi.org/10.1016/j.engappai.2011.09.024
plymouth.publication-statusPublished
plymouth.journalEngineering Applications of Artificial Intelligence
dc.identifier.doi10.1016/j.engappai.2011.09.024
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA12 Engineering
plymouth.organisational-group/Plymouth/Research Groups
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dc.identifier.eissn1873-6769
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1016/j.engappai.2011.09.024
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review


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