Logo do repositório
 
Publicação

Universal learning machine with genetic programming

2019-01-01Documento de conferência Acesso aberto
Ver registo simples
dc.contributor.authorRe, Alessandro
dc.contributor.authorVanneschi, Leonardo
dc.contributor.authorCastelli, Mauro
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.coverage.spatialViena
dc.date.accessioned2019-11-12T05:04:12Z
dc.date.available2019-11-12T05:04:12Z
dc.date.issued2019-01-01
dc.descriptionRe, A., Vanneschi, L., & Castelli, M. (2019). Universal learning machine with genetic programming. In J. J. Merelo, J. Garibaldi, A. Linares-Barranco, K. Madani, K. Warwick, & K. Warwick (Eds.), Proceedings of the 11th International Joint Conference on Computational Intelligence (Vol. 1, pp. 115-122). (IJCCI 2019 - Proceedings of the 11th International Joint Conference on Computational Intelligence). Viena: SciTePress.
dc.description.abstractThis paper presents a proof of concept. It shows that Genetic Programming (GP) can be used as a "universal" machine learning method, that integrates several different algorithms, improving their accuracy. The system we propose, called Universal Genetic Programming (UGP) works by defining an initial population of programs, that contains the models produced by several different machine learning algorithms. The use of elitism allows UGP to return as a final solution the best initial model, in case it is not able to evolve a better one. The use of genetic operators driven by semantic awareness is likely to improve the initial models, by combining and mutating them. On three complex real-life problems, we present experimental evidence that UGP is actually able to improve the models produced by all the studied machine learning algorithms in isolation.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent8
dc.format.extent796508
dc.identifier.doi10.5220/0007808101150122
dc.identifier.isbn9789897583841
dc.identifier.otherPURE: 15379539
dc.identifier.otherPURE UUID: 96a4744c-0c18-48cd-afff-566410dd18d9
dc.identifier.otherORCID: /0000-0002-8793-1451/work/72856144
dc.identifier.otherWOS: 000571773900010
dc.identifier.otherScopus: 85074267111
dc.identifier.otherORCID: /0000-0003-4732-3328/work/151426752
dc.identifier.urihttp://www.scopus.com/inward/record.url?scp=85074267111&partnerID=8YFLogxK
dc.identifier.urlhttps://www.scopus.com/pages/publications/85074267111
dc.identifier.urlhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000571773900010
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSciTePress - Science and Technology Publications
dc.relationinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0022%2F2018/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FCCI-INF%2F29168%2F2017/PT
dc.subjectEnsembles
dc.subjectGenetic programming
dc.subjectGeometric semantic genetic programming
dc.subjectMachine learning
dc.subjectMaster algorithm
dc.subjectArtificial Intelligence
dc.subjectComputational Theory and Mathematics
dc.titleUniversal learning machine with genetic programmingen
dc.typeconference object
degois.publication.firstPage115
degois.publication.lastPage122
degois.publication.titleProceedings of the 11th International Joint Conference on Computational Intelligence
degois.publication.title11th International Joint Conference on Computational Intelligence, IJCCI 2019
degois.publication.volume1
dspace.entity.typePublication
oaire.awardNumberDSAIPA/DS/0022/2018
oaire.awardNumberPTDC/CCI-INF/29168/2017
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0022%2F2018/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FCCI-INF%2F29168%2F2017/PT
oaire.fundingStream3599-PPCDT
oaire.fundingStream3599-PPCDT
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublicationc35c919f-29eb-4019-b809-622c143b6c56
relation.isProjectOfPublication2624e8d1-5a03-474c-b4a2-34987301953a
relation.isProjectOfPublication.latestForDiscovery2624e8d1-5a03-474c-b4a2-34987301953a

Ficheiros

Principais
A mostrar 1 - 1 de 1
Miniatura
Nome:
Universal_Learning_Machine_Genetic_Programming.pdf
Tamanho:
777.84 KB
Formato:
Adobe Portable Document Format
Ver/Abrir

Coleções

NIMS: MagIC - Documentos de conferências internacionais