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Conference Paper: Continuous non-revisiting genetic algorithm with overlapped search sub-region

TitleContinuous non-revisiting genetic algorithm with overlapped search sub-region
Authors
Issue Date2012
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4235
Citation
The 2012 IEEE Congress on Evolutionary Computation (CEC 2012), Brisbane, QLD., Australia, 10-15 June 2012. In IEEE Transactions on Evolutionary Computation, 2012, p. 1-8 How to Cite?
AbstractIn continuous non-revisiting genetic algorithm (cNrGA), search space is partitioned into sub-regions according to the distribution of evaluated solutions. The partitioned subregion serves as mutation range such that the corresponding mutation is adaptive and parameter-less. As pointed out by Chow and Yuen, the boundary condition of the mutation in cNrGA is too restricted that the exploitative power of cNrGA is reduced. In this paper, we tackle this structural problem of cNrGA by a new formulation of mutation range. When sub-region is formulated as which certain overlap exists between adjacent sub-regions, this creates a soft boundary and it allows individual move from a sub-region to another with better fitness. This modified cNrGA is named cNrGA with overlapped search sub-region (cNrGA/OL/OGF). By comparing with another work on this problem, Continuous non-revisiting genetic algorithm with randomly re-partitioned BSP tree (cNrGA/RP/OGF), it has an advantage on processing speed. The proposed algorithm is examined on 34 benchmark functions at dimensions ranging from 2 to 40. The results show that the proposed algorithm is superior to the original cNrGA, cNrGA/RP/OGF and covariance matrix adaptation evolutionary strategy (CMA-ES). © 2012 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/196653
ISBN
ISSN
2015 Impact Factor: 5.908
2015 SCImago Journal Rankings: 4.308

 

DC FieldValueLanguage
dc.contributor.authorChow, CK-
dc.contributor.authorYuen, SY-
dc.date.accessioned2014-04-24T02:10:31Z-
dc.date.available2014-04-24T02:10:31Z-
dc.date.issued2012-
dc.identifier.citationThe 2012 IEEE Congress on Evolutionary Computation (CEC 2012), Brisbane, QLD., Australia, 10-15 June 2012. In IEEE Transactions on Evolutionary Computation, 2012, p. 1-8-
dc.identifier.isbn978-1-4673-1510-4-
dc.identifier.issn1089-778X-
dc.identifier.urihttp://hdl.handle.net/10722/196653-
dc.description.abstractIn continuous non-revisiting genetic algorithm (cNrGA), search space is partitioned into sub-regions according to the distribution of evaluated solutions. The partitioned subregion serves as mutation range such that the corresponding mutation is adaptive and parameter-less. As pointed out by Chow and Yuen, the boundary condition of the mutation in cNrGA is too restricted that the exploitative power of cNrGA is reduced. In this paper, we tackle this structural problem of cNrGA by a new formulation of mutation range. When sub-region is formulated as which certain overlap exists between adjacent sub-regions, this creates a soft boundary and it allows individual move from a sub-region to another with better fitness. This modified cNrGA is named cNrGA with overlapped search sub-region (cNrGA/OL/OGF). By comparing with another work on this problem, Continuous non-revisiting genetic algorithm with randomly re-partitioned BSP tree (cNrGA/RP/OGF), it has an advantage on processing speed. The proposed algorithm is examined on 34 benchmark functions at dimensions ranging from 2 to 40. The results show that the proposed algorithm is superior to the original cNrGA, cNrGA/RP/OGF and covariance matrix adaptation evolutionary strategy (CMA-ES). © 2012 IEEE.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4235-
dc.relation.ispartofIEEE Transactions on Evolutionary Computation-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsIEEE Transactions on Evolutionary Computation. Copyright © Institute of Electrical and Electronics Engineers.-
dc.titleContinuous non-revisiting genetic algorithm with overlapped search sub-region-
dc.typeConference_Paper-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/CEC.2012.6252926-
dc.identifier.scopuseid_2-s2.0-84866872766-
dc.identifier.spage1-
dc.identifier.epage8-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 160603 amended-

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