File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Continuous non-revisiting genetic algorithm with random search space re-partitioning and one-gene-flip mutation

TitleContinuous non-revisiting genetic algorithm with random search space re-partitioning and one-gene-flip mutation
Authors
Issue Date2010
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 6th IEEE World Congress on Computational Intelligence cum IEEE Congress on Evolutionary Computation (WCCI-CEC 2010), Barcelona, Spain, 18-23 July 2010. In IEEE Transactions on Evolutionary Computation, 2010, p. 1-8 How to Cite?
AbstractIn continuous non-revisiting genetic algorithm (cNrGA), the solution set with different order leads to different density estimation and hence different mutation step size. As a result, the performance of cNrGA depends on the order of the evaluated solutions. In this paper, we propose to remove this dependence by a search space re-partitioning strategy. At each iteration, the strategy re-shuffles the solutions into random order. The re-ordered sequence is then used to construct a new density tree, which leads to a new space partition sets. Afterwards, instead of randomly picking a mutant within a partition, a new adaptive one-gene-flip mutation is applied. Motivated from the fact that the proposed adaptive mutation concerns only small amount of partitions, we propose a new density tree construction algorithm. This algorithm refuses to partition the sub-regions which do not contain any individual to be mutated, which simplifies the tree topology as well as speeds up the construction time. The new cNrGA integrated with the proposed re-partitioning strategy (cNrGA/RP/OGF) is examined on 19 benchmark functions at dimensions ranging from 2 to 40. The simulation results show that cNrGA/RP/OGF is significantly superior to the original cNrGA at most of the test functions. Its average performance is also better than those of six benchmark EAs. © 2010 IEEE.
DescriptionSpecial Session on Evolutionary Computer Vision
Persistent Identifierhttp://hdl.handle.net/10722/196678
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.accessioned2014-04-24T02:10:33Z-
dc.date.available2014-04-24T02:10:33Z-
dc.date.issued2010-
dc.identifier.citationThe 6th IEEE World Congress on Computational Intelligence cum IEEE Congress on Evolutionary Computation (WCCI-CEC 2010), Barcelona, Spain, 18-23 July 2010. In IEEE Transactions on Evolutionary Computation, 2010, p. 1-8-
dc.identifier.isbn978-1-4244-6909-3-
dc.identifier.issn1089-778X-
dc.identifier.urihttp://hdl.handle.net/10722/196678-
dc.descriptionSpecial Session on Evolutionary Computer Vision-
dc.description.abstractIn continuous non-revisiting genetic algorithm (cNrGA), the solution set with different order leads to different density estimation and hence different mutation step size. As a result, the performance of cNrGA depends on the order of the evaluated solutions. In this paper, we propose to remove this dependence by a search space re-partitioning strategy. At each iteration, the strategy re-shuffles the solutions into random order. The re-ordered sequence is then used to construct a new density tree, which leads to a new space partition sets. Afterwards, instead of randomly picking a mutant within a partition, a new adaptive one-gene-flip mutation is applied. Motivated from the fact that the proposed adaptive mutation concerns only small amount of partitions, we propose a new density tree construction algorithm. This algorithm refuses to partition the sub-regions which do not contain any individual to be mutated, which simplifies the tree topology as well as speeds up the construction time. The new cNrGA integrated with the proposed re-partitioning strategy (cNrGA/RP/OGF) is examined on 19 benchmark functions at dimensions ranging from 2 to 40. The simulation results show that cNrGA/RP/OGF is significantly superior to the original cNrGA at most of the test functions. Its average performance is also better than those of six benchmark EAs. © 2010 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 random search space re-partitioning and one-gene-flip mutation-
dc.typeConference_Paper-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/CEC.2010.5586046-
dc.identifier.scopuseid_2-s2.0-79959466837-
dc.identifier.spage1-
dc.identifier.epage8-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 160603 amended-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats