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dc.contributor.authorFiorentino, Helenice O.-
dc.contributor.authorCantane, Daniela R.-
dc.contributor.authorSantos, Fernando L. P.-
dc.contributor.authorBannwart, Bettina F.-
dc.date.accessioned2015-11-03T15:28:12Z-
dc.date.accessioned2016-10-25T21:16:53Z-
dc.date.available2015-11-03T15:28:12Z-
dc.date.available2016-10-25T21:16:53Z-
dc.date.issued2014-12-01-
dc.identifierhttp://www.sciencedirect.com/science/article/pii/S0025556414001680-
dc.identifier.citationMathematical Biosciences. New York: Elsevier Science Inc, v. 258, p. 77-84, 2014.-
dc.identifier.issn0025-5564-
dc.identifier.urihttp://hdl.handle.net/11449/129960-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/129960-
dc.description.abstractDengue fever is an infectious disease caused by a virus of the Flaviridae family and transmitted to the person by a mosquito of the genus Aedes aegypti. This disease has been a global public health problem because a single mosquito can infect up to 300 people and between 50 and 100 million people are infected annually on all continents. Thus, dengue fever is currently a subject of research, whether in the search for vaccines and treatments for the disease or efficient and economical forms of mosquito control. The current study aims to study techniques of multiobjective optimization to assist in solving problems involving the control of the mosquito that transmits dengue fever. The population dynamics of the mosquito is studied in order to understand the epidemic phenomenon and suggest strategies of multiobjective programming for mosquito control. A Multiobjective Genetic Algorithm (MGA_DENGUE) is proposed to solve the optimization model treated here and we discuss the computational results obtained from the application of this technique. (C) 2014 Elsevier Inc. All rights reserved.en
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
dc.description.sponsorshipFundação para o Desenvolvimento da UNESP (FUNDUNESP)-
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
dc.description.sponsorshipPROPG UNESP-
dc.format.extent77-84-
dc.language.isoeng-
dc.publisherElsevier B.V.-
dc.sourceWeb of Science-
dc.subjectDengueen
dc.subjectMultiobjective optimizationen
dc.subjectGenetic algorithmen
dc.titleMultiobjective Genetic Algorithm applied to dengue controlen
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationUNESP, IBB Inst Biociencias, Dept Bioestat, Botucatu, SP, Brazil-
dc.description.affiliationUnespUNESP, IBB Inst Biociencias, Dept Bioestat, Botucatu, SP, Brazil-
dc.description.sponsorshipIdFAPESP: 2009/15098-0-
dc.description.sponsorshipIdFAPESP: 2010/07586-6-
dc.description.sponsorshipIdFAPESP: 2014/01604-0-
dc.description.sponsorshipIdFUNDUNESP: 0351/019/13-
dc.description.sponsorshipIdCNPq: 303267/2011-9-
dc.identifier.doihttp://dx.doi.org/10.1016/j.mbs.2014.08.013-
dc.identifier.wosWOS:000348020700008-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofMathematical Biosciences-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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