Optimization of existing equations using a new genetic programming algorithm: application to the shear strength of reinforced concrete beams
| UDC.coleccion | Investigación | es_ES |
| UDC.departamento | Ciencias da Computación e Tecnoloxías da Información | es_ES |
| UDC.endPage | 96 | es_ES |
| UDC.grupoInv | Redes de Neuronas Artificiais e Sistemas Adaptativos -Informática Médica e Diagnóstico Radiolóxico (RNASA - IMEDIR) | es_ES |
| UDC.journalTitle | Advances in Engineering Software | es_ES |
| UDC.startPage | 82 | es_ES |
| UDC.volume | 50 | es_ES |
| dc.contributor.author | Pérez Ordóñez, Juan Luis | |
| dc.contributor.author | Cladera, Antoni | |
| dc.contributor.author | Rabuñal, Juan R. | |
| dc.contributor.author | Martínez-Abella, Fernando | |
| dc.date.accessioned | 2018-09-18T11:48:30Z | |
| dc.date.available | 2018-09-18T11:48:30Z | |
| dc.date.issued | 2012-03-13 | |
| dc.description.abstract | [Abstract] A method based on Genetic Programming (GP) to improve previously known empirical equations is presented. From a set of experimental data, the GP may improve the adjustment of such formulas through the symbolic regression technique. Through a set of restrictions, and the indication of the terms of the expression to be improved, GP creates new individuals. The methodology allows us to study the need of including new variables in the expression. The proposed method is applied to the shear strength of concrete beams. The results show a marked improvement using this methodology in relation to the classic GP and international code procedures. | es_ES |
| dc.description.sponsorship | Ministerio de Ciencia e Innovación; BIA2007-60197 | es_ES |
| dc.description.sponsorship | Ministerio de Ciencia e Innovación; BIA2010-21551 | es_ES |
| dc.description.sponsorship | Xunta de Galicia; 08TMT005CT | es_ES |
| dc.description.sponsorship | Xunta de Galicia; 10TMT034E | es_ES |
| dc.identifier.citation | Pérez JL, Cladera A, Rabuñal JR, Martínez-Abella F. Optimization of existing equations using a new genetic programming algorithm: application to the shear strength of reinforced concrete beams. Adv Eng Softw. 2012;50:82-96 | es_ES |
| dc.identifier.issn | 0965-9978 | |
| dc.identifier.uri | http://hdl.handle.net/2183/21029 | |
| dc.language.iso | eng | es_ES |
| dc.publisher | Elsevier | es_ES |
| dc.relation.uri | https://doi.org/10.1016/j.advengsoft.2012.02.008 | es_ES |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | es_ES |
| dc.rights.accessRights | open access | es_ES |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
| dc.subject | Artificial intelligence | es_ES |
| dc.subject | Genetic programming | es_ES |
| dc.subject | Structural engineering | es_ES |
| dc.subject | Concrete | es_ES |
| dc.subject | Shear strength | es_ES |
| dc.subject | Regression analysis | es_ES |
| dc.title | Optimization of existing equations using a new genetic programming algorithm: application to the shear strength of reinforced concrete beams | es_ES |
| dc.type | journal article | es_ES |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | 11c12f40-1d52-40af-8cb2-bc73190ad1af | |
| relation.isAuthorOfPublication | 397020b4-7e95-43bc-848d-969c5c1bbd7d | |
| relation.isAuthorOfPublication | 1b5d3119-6c7d-4bec-949a-e2d4c3e0471b | |
| relation.isAuthorOfPublication.latestForDiscovery | 11c12f40-1d52-40af-8cb2-bc73190ad1af |
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