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Speeding Up Evolutionary Learning Algorithms using GPUs
dc.contributor.author | Cano, Alberto | |
dc.contributor.author | Zafra, Amelia | |
dc.contributor.author | Ventura Soto, S. | |
dc.date.accessioned | 2013-08-30T10:26:08Z | |
dc.date.available | 2013-08-30T10:26:08Z | |
dc.date.issued | 2010 | |
dc.identifier.uri | http://hdl.handle.net/10396/10874 | |
dc.description.abstract | This paper propose a multithreaded Genetic Programming classi cation evaluation model using NVIDIA CUDA GPUs to reduce the computational time due to the poor perfor- mance in large problems. Two di erent clas- si cation algorithms are benchmarked using UCI Machine Learning data sets. Experi- mental results compare the performance us- ing single and multithreaded Java, C and GPU code and show the e ciency far better obtained by our proposal. | es_ES |
dc.format.mimetype | application/pdf | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | ESTYLF | es_ES |
dc.rights | https://creativecommons.org/licenses/by-nc-nd/4.0/ | es_ES |
dc.source | En: XV Congreso Español Sobre Tecnologías y Lógica Fuzzy, ESTYLF 2010, Huelva, 3 a 5 de febrero de 2010 | es_ES |
dc.subject | GPUs | es_ES |
dc.subject | CUDA | es_ES |
dc.subject | Genetic programming | es_ES |
dc.title | Speeding Up Evolutionary Learning Algorithms using GPUs | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |