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Conference Paper: Continuous non-revisiting genetic algorithm
Title | Continuous non-revisiting genetic algorithm |
---|---|
Authors | |
Issue Date | 2009 |
Publisher | Institute 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? |
Abstract | The 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 Identifier | http://hdl.handle.net/10722/196710 |
ISBN | |
ISSN | 2023 Impact Factor: 11.7 2023 SCImago Journal Rankings: 5.209 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Yuen, SY | - |
dc.contributor.author | Chow, CK | - |
dc.date.accessioned | 2014-04-24T02:10:35Z | - |
dc.date.available | 2014-04-24T02:10:35Z | - |
dc.date.issued | 2009 | - |
dc.identifier.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 | - |
dc.identifier.isbn | 978-1-4244-2958-5 | - |
dc.identifier.issn | 1089-778X | - |
dc.identifier.uri | http://hdl.handle.net/10722/196710 | - |
dc.description.abstract | The 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.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4235 | - |
dc.relation.ispartof | IEEE Transactions on Evolutionary Computation | - |
dc.rights | ©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. | - |
dc.title | Continuous non-revisiting genetic algorithm | - |
dc.type | Conference_Paper | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1109/CEC.2009.4983172 | - |
dc.identifier.scopus | eid_2-s2.0-70450060067 | - |
dc.identifier.spage | 1896 | - |
dc.identifier.epage | 1903 | - |
dc.identifier.isi | WOS:000274803100250 | - |
dc.publisher.place | United States | - |
dc.customcontrol.immutable | sml 160602 amended | - |
dc.identifier.issnl | 1089-778X | - |