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Pseudo neural networks synthesized via evolutionary symbolic regression for Pima diabetes

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dc.title Pseudo neural networks synthesized via evolutionary symbolic regression for Pima diabetes en
dc.contributor.author Komínková Oplatková, Zuzana
dc.contributor.author Šenkeřík, Roman
dc.relation.ispartof MENDEL 2016
dc.identifier.issn 1803-3814 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 9788021453654
dc.date.issued 2016
dc.citation.spage 153
dc.citation.epage 158
dc.event.title 22nd International Conference on Soft Computing: Evolutionary Computation, Genetic Programming, Swarm Intelligence, Fuzzy Logic, Neural Networks, Chaos, Bayesian Methods, Intelligent Image Processing, Bio-Inspired Robotics, MENDEL 2016
dc.event.location Brno
utb.event.state-en Czech Republic
utb.event.state-cs Česká republika
dc.event.sdate 2016-06-08
dc.event.edate 2016-06-10
dc.type conferenceObject
dc.language.iso en
dc.publisher Brno University of Technology
dc.subject Analytic programming en
dc.subject Differential evolution en
dc.subject Evolutionary symbolic regression en
dc.subject Pima diabetes set en
dc.subject Pseudo neural networks en
dc.description.abstract This research deals with pseudo neural networks which were applied for solving Pima diabetes set. Pseudo neural networks are complex expressions synthesized by means of an evolutionary symbolic regression technique - analytic programming (AP). It represents a novel approach to classification when a relation between inputs and outputs is created. The inspiration came from classical artificial neural networks where such a relation between inputs and outputs is based on the mathematical transfer functions and optimized numerical weights. AP will synthesize a whole expression at once. There is also an advantage of suitable feature set selection during the same step of pseudo neural net synthesis. For experimentation, Differential Evolution (DE) for the main procedure and also for meta-evolution version of analytic programming (AP) was used. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1007120
utb.identifier.obdid 43876374
utb.identifier.scopus 2-s2.0-85014885905
utb.source d-scopus
dc.date.accessioned 2017-08-01T08:27:14Z
dc.date.available 2017-08-01T08:27:14Z
dc.description.sponsorship CZ.1.05/2.1.00/03.0089, ERDF, European Regional Development Fund
utb.contributor.internalauthor Komínková Oplatková, Zuzana
utb.contributor.internalauthor Šenkeřík, Roman
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