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dc.contributor.authorGeyer, H.de
dc.contributor.authorKracht, C.de
dc.contributor.authorSchulz, S.de
dc.contributor.authorUlbig, P.de
dc.date.accessioned2004-12-07T08:19:39Z-
dc.date.available2004-12-07T08:19:39Z-
dc.date.created1998de
dc.date.issued1998-11-08de
dc.identifier.urihttp://hdl.handle.net/2003/5349-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-5646-
dc.description.abstractThe prediction of certain thermodynamic properties of pure substances and mixtures with calculation methods is a frequent task during the process design in chemical engineering. Group contribution models divide the molecules into functional groups and if the model parameters for theses groups are known, predictions of compounds that comprise these groups are possible. The model parameters have to be fitted to experimental data, which leads to a multi-parameter multimodal optimization problem. In this paper the optimization of the tuning parameters of Evolution Strategies and different methods of parameter fitting regarding the number of parameters are presented.en
dc.format.extent3755032 bytes-
dc.format.extent897557 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/postscript-
dc.language.isoende
dc.publisherUniversität Dortmundde
dc.relation.ispartofseriesReihe Computational Intelligence ; 42de
dc.subject.ddc004de
dc.titleOptimum tuning parameters for Encapsulated Evolution Strategies : Results for a nonlinear regression problemen
dc.typeTextde
dc.type.publicationtypereport-
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 531

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