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Article: Parameter control system of evolutionary algorithm that is aided by the entire search history

TitleParameter control system of evolutionary algorithm that is aided by the entire search history
Authors
Issue Date2012
Citation
Applied Soft Computing Journal, 2012, v. 12 n. 9, p. 3063-3078 How to Cite?
AbstractIn solving problems with evolutionary algorithms (EAs), the performance of the EA will be affected by its properties. As the properties of EA depend on the parameter setting, users need to tune the parameters to optimize the performance on different problems. In the case that the user does not have any prior knowledge of the problem, parameter tuning is very difficult and time consuming. One needs to try different combinations of parameter values to find the best setting. To solve this problem, one way is to control the parameters during the EA run. This paper proposes a new adaptive parameter control system, called Parameter Control system using entire Search History (PCSH). It is a general add-on system which is not restricted to a specific class of EA. Users are only required to know the range of the parameters. It automatically adjusts the parameters of an EA according to the entire search history, in a parameter-less manner. To illustrate the performance of PCSH, it is applied to control the parameters of three common classes of EAs: (1) canonical Genetic Algorithm (GA), (2) Particle Swarm Optimization (PSO) and (3) Differential Evolution (DE). For GA, we show that PCSH can automatically control the crossover operator, crossover values (uniformly sampled from the range) and mutation operator. For DE, we show that PCSH can automatically control the crossover operator, crossover values and the differential amplification factor (uniformly sampled from the ranges). For PSO, we show that PCSH can automatically control the two learning factors and the inertia weight (uniformly sampled from the range). Moreover, no special provision is needed at the initialization. 34 benchmark functions are used to evaluate the performance comprehensively. The test results show that, in most of the benchmark functions, the performance of the test EAs are improved or similar after adopting PCSH. It shows that PCSH keeps or improves the performance of the test EAs while relieving the heavy burden of the algorithm designer on the setting of some parameters. © 2012 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/196652
ISSN
2015 Impact Factor: 2.857
2015 SCImago Journal Rankings: 1.763
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLeung, SW-
dc.contributor.authorYuen, SY-
dc.contributor.authorChow, CK-
dc.date.accessioned2014-04-24T02:10:31Z-
dc.date.available2014-04-24T02:10:31Z-
dc.date.issued2012-
dc.identifier.citationApplied Soft Computing Journal, 2012, v. 12 n. 9, p. 3063-3078-
dc.identifier.issn1568-4946-
dc.identifier.urihttp://hdl.handle.net/10722/196652-
dc.description.abstractIn solving problems with evolutionary algorithms (EAs), the performance of the EA will be affected by its properties. As the properties of EA depend on the parameter setting, users need to tune the parameters to optimize the performance on different problems. In the case that the user does not have any prior knowledge of the problem, parameter tuning is very difficult and time consuming. One needs to try different combinations of parameter values to find the best setting. To solve this problem, one way is to control the parameters during the EA run. This paper proposes a new adaptive parameter control system, called Parameter Control system using entire Search History (PCSH). It is a general add-on system which is not restricted to a specific class of EA. Users are only required to know the range of the parameters. It automatically adjusts the parameters of an EA according to the entire search history, in a parameter-less manner. To illustrate the performance of PCSH, it is applied to control the parameters of three common classes of EAs: (1) canonical Genetic Algorithm (GA), (2) Particle Swarm Optimization (PSO) and (3) Differential Evolution (DE). For GA, we show that PCSH can automatically control the crossover operator, crossover values (uniformly sampled from the range) and mutation operator. For DE, we show that PCSH can automatically control the crossover operator, crossover values and the differential amplification factor (uniformly sampled from the ranges). For PSO, we show that PCSH can automatically control the two learning factors and the inertia weight (uniformly sampled from the range). Moreover, no special provision is needed at the initialization. 34 benchmark functions are used to evaluate the performance comprehensively. The test results show that, in most of the benchmark functions, the performance of the test EAs are improved or similar after adopting PCSH. It shows that PCSH keeps or improves the performance of the test EAs while relieving the heavy burden of the algorithm designer on the setting of some parameters. © 2012 Elsevier B.V. All rights reserved.-
dc.languageeng-
dc.relation.ispartofApplied Soft Computing Journal-
dc.titleParameter control system of evolutionary algorithm that is aided by the entire search history-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.asoc.2012.05.008-
dc.identifier.scopuseid_2-s2.0-84863472179-
dc.identifier.volume12-
dc.identifier.issue9-
dc.identifier.spage3063-
dc.identifier.epage3078-
dc.identifier.isiWOS:000306107900031-

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