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Conference Paper: Continuous non-revisiting genetic algorithm

TitleContinuous 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. 1896-1903 How to Cite?
AbstractThe non-revisiting genetic algorithm (NrGA) is extended to handle continuous search space. The extended NrGA model, Continuous NrGA (cNrGA), employs the same tree-structure archive of NrGA to memorize the evaluated solutions, in which the search space is divided into non-overlapped partitions according to the distribution of the solutions. cNrGA is a bi-modulus evolutionary algorithm consisting of the genetic algorithm module (GAM) and the adaptive mutation module (AMM). When GAM generates an offspring, the offspring is sent to AMM and is mutated according to the density of the solutions stored in the memory archive. For a point in the search space with high solution-density, it infers a high probability that the point is close to the optimum and hence a near search is suggested. Alternatively, a far search is recommended for a point with low solution-density. Benefitting from the space partitioning scheme, a fast solution-density approximation is obtained. Also, the adaptive mutation scheme naturally avoid the generation of out-of-bound solutions. The performance of cNrGA is tested on 14 benchmark functions on dimensions ranging from 2 to 40. It is compared with real coded GA, differential evolution, covariance matrix adaptation evolution strategy and two improved particle swarm optimization. The simulation results show that cNrGA outperforms the other algorithms for multi-modal function optimization.
Persistent Identifierhttp://hdl.handle.net/10722/196710
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. 1896-1903-
dc.identifier.isbn978-1-4244-2958-5-
dc.identifier.issn1089-778X-
dc.identifier.urihttp://hdl.handle.net/10722/196710-
dc.description.abstractThe non-revisiting genetic algorithm (NrGA) is extended to handle continuous search space. The extended NrGA model, Continuous NrGA (cNrGA), employs the same tree-structure archive of NrGA to memorize the evaluated solutions, in which the search space is divided into non-overlapped partitions according to the distribution of the solutions. cNrGA is a bi-modulus evolutionary algorithm consisting of the genetic algorithm module (GAM) and the adaptive mutation module (AMM). When GAM generates an offspring, the offspring is sent to AMM and is mutated according to the density of the solutions stored in the memory archive. For a point in the search space with high solution-density, it infers a high probability that the point is close to the optimum and hence a near search is suggested. Alternatively, a far search is recommended for a point with low solution-density. Benefitting from the space partitioning scheme, a fast solution-density approximation is obtained. Also, the adaptive mutation scheme naturally avoid the generation of out-of-bound solutions. The performance of cNrGA is tested on 14 benchmark functions on dimensions ranging from 2 to 40. It is compared with real coded GA, differential evolution, covariance matrix adaptation evolution strategy and two improved particle swarm optimization. The simulation results show that cNrGA outperforms the other algorithms for multi-modal function optimization.-
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-
dc.typeConference_Paper-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/CEC.2009.4983172-
dc.identifier.scopuseid_2-s2.0-70450060067-
dc.identifier.spage1896-
dc.identifier.epage1903-
dc.identifier.isiWOS:000274803100250-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 160602 amended-

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