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Conference Paper: A study of operator and parameter choices in non-revisiting genetic algorithm

TitleA study of operator and parameter choices in non-revisiting genetic algorithm
Authors
Issue Date2009
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 2009 IEEE Congress on Evolutionary Computation (CEC 2009), Trondheim, Norway, 18-21 May 2009. In IEEE Transactions on Evolutionary Computation, 2009, p. 2977-2984 How to Cite?
AbstractWe study empirically the effects of operator and parameter choices on the performance of the non-revisiting genetic algorithm (NrGA). For a suite of 14 benchmark functions that include both uni-modal and multi-modal functions, it is found that NrGA is insensitive to the axis resolution of the problem, which is a good feature. From the empirical experiments, for operators, it is found that crossover is an essential operator for NrGA, and the best crossover operator is uniform crossover, while the best selection operator is elitist selection. For parameters, a small population, with a population size strictly larger than 1, should be used; the performance is monotonically increasing with crossover rate and the optimal crossover rate is 0.5. The results of this paper provide empirical guidelines for operator designs and parameter settings of NrGA. © 2009 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/196711
ISBN
ISSN
2015 Impact Factor: 5.908
2015 SCImago Journal Rankings: 4.308
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYuen, SY-
dc.contributor.authorChow, CK-
dc.date.accessioned2014-04-24T02:10:35Z-
dc.date.available2014-04-24T02:10:35Z-
dc.date.issued2009-
dc.identifier.citationThe 2009 IEEE Congress on Evolutionary Computation (CEC 2009), Trondheim, Norway, 18-21 May 2009. In IEEE Transactions on Evolutionary Computation, 2009, p. 2977-2984-
dc.identifier.isbn978-1-4244-2958-5-
dc.identifier.issn1089-778X-
dc.identifier.urihttp://hdl.handle.net/10722/196711-
dc.description.abstractWe study empirically the effects of operator and parameter choices on the performance of the non-revisiting genetic algorithm (NrGA). For a suite of 14 benchmark functions that include both uni-modal and multi-modal functions, it is found that NrGA is insensitive to the axis resolution of the problem, which is a good feature. From the empirical experiments, for operators, it is found that crossover is an essential operator for NrGA, and the best crossover operator is uniform crossover, while the best selection operator is elitist selection. For parameters, a small population, with a population size strictly larger than 1, should be used; the performance is monotonically increasing with crossover rate and the optimal crossover rate is 0.5. The results of this paper provide empirical guidelines for operator designs and parameter settings of NrGA. © 2009 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.titleA study of operator and parameter choices in non-revisiting genetic algorithm-
dc.typeConference_Paper-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/CEC.2009.4983318-
dc.identifier.scopuseid_2-s2.0-70450080718-
dc.identifier.spage2977-
dc.identifier.epage2984-
dc.identifier.isiWOS:000274803101131-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 160602 amended-

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