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Conference Paper: Applying non-revisiting genetic algorithm to traveling salesman problem

TitleApplying non-revisiting genetic algorithm to traveling salesman problem
Authors
Issue Date2008
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 2008 IEEE Congress on Evolutionary Computation (CEC 2008), Hong Kong, China, 1-6 June 2008. In IEEE Transactions on Evolutionary Computation, 2008, p. 2217-2224 How to Cite?
AbstractIn [1], we propose non-revisiting genetic algorithm (NrGA) and apply it to a set of bench mark real valued test functions. NrGA has the advantage that it is non-revisiting, i.e. a visited point will not be visited again. This provides an automatic mechanism for diversity maintenance which does not suffer from premature convergence. Another advantage is that it supports a parameter-less adaptive mutation mechanism. In this paper, we show how NrGA can be adapted to a real world combinatorial optimization problem - the famous traveling salesman problem (TSP). Comparison with genetic algorithm (GA) (with revisits and standard mutation) is made. It is shown that NrGA gives superior performance compared to GA. Moreover, it gives the same stable performance using different types of mutation operators. Moreover, turning off GA's mutation operator but only use the NrGA inherent parameter-less adaptive mutation gives the best performance. © 2008 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/196702
ISBN
ISSN
2015 Impact Factor: 5.908
2015 SCImago Journal Rankings: 4.308

 

DC FieldValueLanguage
dc.contributor.authorYuen, SY-
dc.contributor.authorChow, CK-
dc.date.accessioned2014-04-24T02:10:34Z-
dc.date.available2014-04-24T02:10:34Z-
dc.date.issued2008-
dc.identifier.citationThe 2008 IEEE Congress on Evolutionary Computation (CEC 2008), Hong Kong, China, 1-6 June 2008. In IEEE Transactions on Evolutionary Computation, 2008, p. 2217-2224-
dc.identifier.isbn978-1-4244-1822-0-
dc.identifier.issn1089-778X-
dc.identifier.urihttp://hdl.handle.net/10722/196702-
dc.description.abstractIn [1], we propose non-revisiting genetic algorithm (NrGA) and apply it to a set of bench mark real valued test functions. NrGA has the advantage that it is non-revisiting, i.e. a visited point will not be visited again. This provides an automatic mechanism for diversity maintenance which does not suffer from premature convergence. Another advantage is that it supports a parameter-less adaptive mutation mechanism. In this paper, we show how NrGA can be adapted to a real world combinatorial optimization problem - the famous traveling salesman problem (TSP). Comparison with genetic algorithm (GA) (with revisits and standard mutation) is made. It is shown that NrGA gives superior performance compared to GA. Moreover, it gives the same stable performance using different types of mutation operators. Moreover, turning off GA's mutation operator but only use the NrGA inherent parameter-less adaptive mutation gives the best performance. © 2008 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.titleApplying non-revisiting genetic algorithm to traveling salesman problem-
dc.typeConference_Paper-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/CEC.2008.4631093-
dc.identifier.scopuseid_2-s2.0-55749096202-
dc.identifier.spage2217-
dc.identifier.epage2224-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 160602 amended-

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