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dc.contributor.authorDroste, Stefande
dc.contributor.authorHeutelbeck, Dominicde
dc.contributor.authorWegener, Ingode
dc.date.accessioned2004-12-07T08:20:28Z-
dc.date.available2004-12-07T08:20:28Z-
dc.date.created2000de
dc.date.issued2001-10-17de
dc.identifier.urihttp://hdl.handle.net/2003/5393-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-15246-
dc.description.abstractWhen genetic programming (GP)is used to find programs with Boolean inputs and outputs, ordered binary decision diagrams (OBDDs) are often used successfully. In all known OBDD-based GP-systems the variable ordering, a crucial factor for the size of OBDDs, is preset to an optimal ordering of the known test function. Certainly this cannot be done in practical applications, where the function to learn and hence its optimal variable ordering are unknown. Here, the first GP-system is presented that evolves the variable ordering of the OBDDs and the OBDDs itself by using a distributed hybrid approach. For the experiments presented the unavoidable size increase compared to the optimal variable ordering is quite small. Hence,this approach is a big step towards learning well-generalizing Boolean functions.en
dc.format.extent191777 bytes-
dc.format.extent546013 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/postscript-
dc.language.isoende
dc.publisherUniversität Dortmundde
dc.relation.ispartofseriesReihe Computational Intelligence ; 90de
dc.subject.ddc004de
dc.titleDistributed Hybrid Genetic Programming for Learning Boolean Functionsen
dc.typeTextde
dc.type.publicationtypereport-
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 531

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