Evolving Teams of Multiple Predictors with Genetic Programming

dc.contributor.authorBanzhaf, Wolfgangde
dc.contributor.authorBrameier, Markusde
dc.date.accessioned2004-12-07T08:20:43Z
dc.date.available2004-12-07T08:20:43Z
dc.date.created2001de
dc.date.issued2001-10-29de
dc.description.abstractThis paper reports on the evolution of GP teams in different classiffication and regression problems and compares dierent methods for combining the outputs of the team programs. These include hybrid approaches where (1) a neural network is used to optimize the weights of programs in a team for a common decision and (2) a real-numbered vector of weights (the representation of evolution strategies) is evolved with each team in parallel. The cooperative team approach results in an improved training and generalization performance compared to the standard GP method. The higher computational overhead of coevolving several genetic programs is counteracted by using a fast variant of linear GP. In particular, the processing time of linear genetic programs is reduced signicantly by removing intron code before program execution.en
dc.format.extent349043 bytes
dc.format.extent463459 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.identifier.urihttp://hdl.handle.net/2003/5404
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-15259
dc.language.isoende
dc.publisherUniversität Dortmundde
dc.relation.ispartofseriesReihe Computational Intelligence ; 105de
dc.subject.ddc004de
dc.titleEvolving Teams of Multiple Predictors with Genetic Programmingen
dc.typeTextde
dc.type.publicationtypereport
dcterms.accessRightsopen access

Files

Original bundle
Now showing 1 - 2 of 2
Loading...
Thumbnail Image
Name:
ci105.pdf
Size:
340.86 KB
Format:
Adobe Portable Document Format
Description:
DNB
No Thumbnail Available
Name:
ci105.ps
Size:
452.6 KB
Format:
Postscript Files