On the influence of different number generators on results of the symbolic regression

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dc.contributor.authorGajdoš, Petr
dc.contributor.authorZelinka, Ivan
dc.date.accessioned2014-05-14T13:53:18Z
dc.date.available2014-05-14T13:53:18Z
dc.date.issued2014
dc.description.abstractThis paper deals with mutual comparison of different pseudorandom number generators and its impact on the performance of selected algorithms of the symbolic regression. In this paper we discuss the use of genetic programming (GP) with use of chaotic systems as well as its main attributes and universal features. It is also explained why deterministic chaos is used and compared here with standard pseudorandom number generators. Based on the characterization of deterministic chaos, universal features of that kind of behavior are explained. In the second part of the paper we discuss selected examples of GP powered by deterministic chaos and classical pseudorandom number generators and its results.cs
dc.description.firstpage641cs
dc.description.issue4cs
dc.description.lastpage650cs
dc.description.sourceWeb of Sciencecs
dc.description.volume18cs
dc.identifier.citationSoft Computing. 2014, vol. 18, no. 4, p. 641-650.cs
dc.identifier.doi10.1007/s00500-013-1172-x
dc.identifier.issn1432-7643
dc.identifier.issn1433-7479
dc.identifier.urihttp://hdl.handle.net/10084/101795
dc.identifier.wos000333030800004
dc.language.isoencs
dc.publisherSpringercs
dc.relation.ispartofseriesSoft Computingcs
dc.relation.urihttp://dx.doi.org/10.1007/s00500-013-1172-xcs
dc.rights© Springer-Verlag Berlin Heidelberg 2014cs
dc.subjectsymbolic regressioncs
dc.subjectrandom numberscs
dc.subjectchaos theorycs
dc.subjectmersenne twistercs
dc.subjectbifurcationcs
dc.titleOn the influence of different number generators on results of the symbolic regressioncs
dc.typearticlecs
dc.type.statusPeer-reviewedcs

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