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dc.contributor.authorFernández, J.C.
dc.contributor.authorGutiérrez, P.A.
dc.contributor.authorHervás-Martínez, César
dc.contributor.authorMartínez, Francisco J.
dc.date.accessioned2015-10-08T07:27:09Z
dc.date.available2015-10-08T07:27:09Z
dc.date.issued2015-10-08
dc.identifier.urihttp://hdl.handle.net/10396/12975
dc.description.abstractThe main objective of this work is to automatically design neural network models with sigmoidal basis units for classification tasks, so that classifiers are obtained in the most balanced way possible in terms of CCR and Sensitivity (given by the lowest percentage of examples correctly predicted to belong to each class). We present a Memetic Pareto Evolutionary NSGA2 (MPENSGA2) approach based on the Pareto-NSGAII evolution (PNSGAII) algorithm. We propose to augmente it with a local search using the improved Rprop—IRprop algorithm for the prediction of growth/no growth of L. monocytogenes as a function of the storage temperature, pH, citric (CA) and ascorbic acid (AA). The results obtained show that the generalization ability can be more efficiently improved within a framework that is multi-objective instead of a within a single-objective one.es_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenges_ES
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/es_ES
dc.source8th International Conference on Hybrid Intelligent Systems (HIS 2008), September 10-12, 2008, Barcelona, Spaines_ES
dc.subjectNeural network modelses_ES
dc.subjectMemetic Paretoes_ES
dc.titleMemetic Pareto Evolutionary Artificial Neural Networks for the determination of growth limits of Listeria Monocytogeneses_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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