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dc.contributor.authorBanzhaf, Wolfgangde
dc.contributor.authorBrameier, Markusde
dc.date.accessioned2004-12-07T08:19:34Z-
dc.date.available2004-12-07T08:19:34Z-
dc.date.created1998de
dc.date.issued1998-11-08de
dc.identifier.urihttp://hdl.handle.net/2003/5344-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-5014-
dc.description.abstractWe 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.extent176818 bytes-
dc.format.extent196553 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/postscript-
dc.language.isoende
dc.publisherUniversität Dortmundde
dc.relation.ispartofseriesReihe Computational Intelligence ; 43de
dc.subject.ddc004de
dc.titleA Comparison of Genetic Programming and Neural Networks in Medical Data Analysisen
dc.typeTextde
dc.type.publicationtypereport-
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 531

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