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Conference Paper: A non-revisiting genetic algorithm

TitleA non-revisiting genetic algorithm
Authors
Issue Date2007
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 2007 IEEE Congress on Evolutionary Computation (CEC 2007), Singapore, 25-28 September 2007. In IEEE Transactions on Evolutionary Computation, 2007, p. 4583-4590 How to Cite?
AbstractGenetic Algorithm (GA) is a revisiting stochastic algorithm. In other words, a solution that has been visited before may be revisited. The fitness of the solution has to be evaluated each time. Since fitness evaluation is the most computationally intensive process in the execution of the GA, revisits should be minimized or eliminated. In this paper, a novel dynamic binary partitioning tree archive is proposed to eliminate all revisits. It works as follows: When the GA generates a solution, the tree is accessed. A leaf node is appended to the tree if the solution has not been visited before and so has no record in the tree. Otherwise, a search is initiated from the leaf node that is the duplicate to the solution to find the nearest neighbor solution in the search space that is not visited. During this process, whole sub-trees may be pruned if all the leaf nodes it contains are visited. The search naturally implements a self adaptive mutation mechanism. Hence the GA requires no other mutation parameter or mutation scheme. Experimental results reveal that this new GA is superior in performance compared with the standard GA with revisits, and the tree archive is not memory intensive. © 2007 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/196701
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.issued2007-
dc.identifier.citationThe 2007 IEEE Congress on Evolutionary Computation (CEC 2007), Singapore, 25-28 September 2007. In IEEE Transactions on Evolutionary Computation, 2007, p. 4583-4590-
dc.identifier.isbn978-1-4244-1339-3-
dc.identifier.issn1089-778X-
dc.identifier.urihttp://hdl.handle.net/10722/196701-
dc.description.abstractGenetic Algorithm (GA) is a revisiting stochastic algorithm. In other words, a solution that has been visited before may be revisited. The fitness of the solution has to be evaluated each time. Since fitness evaluation is the most computationally intensive process in the execution of the GA, revisits should be minimized or eliminated. In this paper, a novel dynamic binary partitioning tree archive is proposed to eliminate all revisits. It works as follows: When the GA generates a solution, the tree is accessed. A leaf node is appended to the tree if the solution has not been visited before and so has no record in the tree. Otherwise, a search is initiated from the leaf node that is the duplicate to the solution to find the nearest neighbor solution in the search space that is not visited. During this process, whole sub-trees may be pruned if all the leaf nodes it contains are visited. The search naturally implements a self adaptive mutation mechanism. Hence the GA requires no other mutation parameter or mutation scheme. Experimental results reveal that this new GA is superior in performance compared with the standard GA with revisits, and the tree archive is not memory intensive. © 2007 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 non-revisiting genetic algorithm-
dc.typeConference_Paper-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/CEC.2007.4425072-
dc.identifier.scopuseid_2-s2.0-55749094180-
dc.identifier.spage4583-
dc.identifier.epage4590-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 160602 amended-

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