A Comparison of Genetic Programming and Neural Networks in Medical Data Analysis

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DC Field	Value	Language
dc.contributor.author	Banzhaf, Wolfgang	de
dc.contributor.author	Brameier, Markus	de
dc.date.accessioned	2004-12-07T08:19:34Z	-
dc.date.available	2004-12-07T08:19:34Z	-
dc.date.created	1998	de
dc.date.issued	1998-11-08	de
dc.identifier.uri	http://hdl.handle.net/2003/5344	-
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-5014	-
dc.description.abstract	We apply an interpreting variant of linear genetic programming to several diagnosis problems in medicine. We compare our results to results obtained with neural networks and argue that genetic programming is able to show similar performances in classification and generalization even when using a relatively small number of generations. Finally, an eficient algorithm for the elimination of introns in linear genetic programs is presented.	en
dc.format.extent	176818 bytes	-
dc.format.extent	196553 bytes	-
dc.format.mimetype	application/pdf	-
dc.format.mimetype	application/postscript	-
dc.language.iso	en	de
dc.publisher	Universität Dortmund	de
dc.relation.ispartofseries	Reihe Computational Intelligence ; 43	de
dc.subject.ddc	004	de
dc.title	A Comparison of Genetic Programming and Neural Networks in Medical Data Analysis	en
dc.type	Text	de
dc.type.publicationtype	report	-
dcterms.accessRights	open access	-
Appears in Collections:	Sonderforschungsbereich (SFB) 531

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