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Towards the use of genetic programming in the ecological modelling of mosquito population dynamics

2020-12Artigo científico Acesso aberto
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dc.contributor.authorAzzali, Irene
dc.contributor.authorVanneschi, Leonardo
dc.contributor.authorMosca, Andrea
dc.contributor.authorBertolotti, Luigi
dc.contributor.authorGiacobini, Mario
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblSpringer Science Business Media
dc.date.accessioned2022-11-30T22:08:40Z
dc.date.available2022-11-30T22:08:40Z
dc.date.issued2020-12
dc.descriptionAzzali, I., Vanneschi, L., Mosca, A., Bertolotti, L., & Giacobini, M. (2020). Towards the use of genetic programming in the ecological modelling of mosquito population dynamics. Genetic Programming And Evolvable Machines. https://doi.org/10.1007/s10710-019-09374-0
dc.description.abstractPredictive algorithms are powerful tools to support infection surveillance plans based on the monitoring of vector abundance. In this article, we explore the use of genetic programming (GP) to build a predictive model of mosquito abundance based on environmental and climatic variables. We claim, in fact, that the heterogeneity and complexity of this kind of dataset demands algorithms capable of discovering complex relationships among variables. For this reason, we benchmarked GP performance with state of the art machine learning predictive algorithms. In order to provide a real exploitable model of mosquito abundance, we trained GP and the other algorithms on mosquito collections from 2002 to 2005 and we tested the predictive ability in 2006 collections. Results reveal that, among the studied methods, GP has the best performance in terms of accuracy and generalization ability. Moreover, the intrinsic feature selection and readability of the solution provided by GP offer the possibility of a biological interpretation of the model which highlights known or new behaviours responsible for mosquito abundance. GP, therefore, reveals to be a promising tool in the field of ecological modelling, opening the way to the use of a vector based GP approach (VE-GP) which may be more appropriate and beneficial for the problems in analysis.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent14
dc.format.extent476147
dc.identifier.doi10.1007/s10710-019-09374-0
dc.identifier.issn1389-2576
dc.identifier.otherPURE: 16446465
dc.identifier.otherPURE UUID: 7d147a2a-a2ef-4dfd-b158-76aaefe57bcc
dc.identifier.otherScopus: 85077568943
dc.identifier.otherWOS: 000574124000001
dc.identifier.otherORCID: /0000-0003-4732-3328/work/151426756
dc.identifier.urihttp://hdl.handle.net/10362/145908
dc.identifier.urlhttps://www.scopus.com/pages/publications/85077568943
dc.identifier.urlhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000574124000001
dc.language.isoeng
dc.peerreviewedyes
dc.subjectEcological modelling
dc.subjectGenetic programming
dc.subjectMachine learning
dc.subjectRegression
dc.subjectSoftware
dc.subjectTheoretical Computer Science
dc.subjectHardware and Architecture
dc.subjectComputer Science Applications
dc.subjectSDG 15 - Life on Land
dc.titleTowards the use of genetic programming in the ecological modelling of mosquito population dynamicsen
dc.typejournal article
degois.publication.firstPage
degois.publication.issue4
degois.publication.lastPage
degois.publication.titleGenetic Programming And Evolvable Machines
degois.publication.volume21
dspace.entity.typePublication
rcaap.rightsopenAccess

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NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)