Genetic Programming with Guaranteed Quality

dc.contributor.author	Droste, Stefan	de
dc.date.accessioned	2004-12-07T08:19:13Z
dc.date.available	2004-12-07T08:19:13Z
dc.date.issued	1998-11-08	de
dc.description.abstract	When using genetic programming (GP) or other techniques that try to approximate unknown functions, the principle of Occam's razor is often applied: find the simplest function that explains the given data, as it is assumed to be the best approximation for the unknown function. Using a well-known result from learning theory, it is shown in this paper, how Occam's razor can help GP in finding functions, so that the number of functions that differ from the unknown function by more than a certain degree can be bounded theoretically. Experiments show how these bounds can be used to get guaranteed quality assurances for practical applications, even though they are much too conservative.	en
dc.format.extent	191542 bytes
dc.format.extent	193791 bytes
dc.format.mimetype	application/pdf
dc.format.mimetype	application/postscript
dc.identifier.uri	http://hdl.handle.net/2003/5321
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-15266
dc.language.iso	en	de
dc.publisher	Universität Dortmund	de
dc.relation.ispartofseries	Reihe Computational Intelligence ; 15	de
dc.subject.ddc	004	de
dc.title	Genetic Programming with Guaranteed Quality	en
dc.type	Text	de
dc.type.publicationtype	report
dcterms.accessRights	open access

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