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dc.contributor.author Hlynka, Markian D. en_US
dc.date.accessioned 2007-05-22T15:11:55Z
dc.date.available 2007-05-22T15:11:55Z
dc.date.issued 1999-08-01T00:00:00Z en_US
dc.identifier.uri http://hdl.handle.net/1993/1983
dc.description.abstract One of the major stumbling blocks of neural networks is the difficulty of designing the networks. Networks must be created by experts who understand both the problem domain and the process of developing neural networks. For complex problems, the process, even for experts, can be an intuitive rather than ratiocinative process. Evolutionary and genetic algorithms are a robust, probabilistic search strategy that excel in large, complex problem spaces. Research involving the application of evolutionary algorithms to neural networks for purposes of both training and selection of an optimal network has been carried out. The focus of such research, however, has been to generate an optimal network of a given structure. No generic framework exists which allows for the automation of the network creation process--the selection and design of the architecture--for a particular problem. This thesis is concerned with the design of such an evolutionary framework. The system is subsequently evaluated with backpropagation networks on an unknown data set. A new method of evolution, probabilistic Lamarkian Learning transfer, appears to produce the desired results. en_US
dc.format.extent 6898989 bytes
dc.format.extent 184 bytes
dc.format.mimetype application/pdf
dc.format.mimetype text/plain
dc.language en en_US
dc.language.iso en_US
dc.rights info:eu-repo/semantics/openAccess
dc.title A framework for an automated neural network designer using evolutionary algorithms en_US
dc.type info:eu-repo/semantics/masterThesis
dc.degree.discipline Computer Science en_US
dc.degree.level Master of Science (M.Sc.) en_US


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