Use este identificador para citar ou linkar para este item:
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1131717Registro completo de metadados
| Campo DC | Valor | Idioma |
|---|---|---|
| dc.contributor.author | FONTOURA, D. de C. N. da | |
| dc.contributor.author | CAMARGO, S. da S. | |
| dc.contributor.author | TORRES JUNIOR, R. A. de A. | |
| dc.contributor.author | CARVALHO, H. G. de | |
| dc.contributor.author | CARDOSO, F. F. | |
| dc.date.accessioned | 2021-05-07T02:10:19Z | - |
| dc.date.available | 2021-05-07T02:10:19Z | - |
| dc.date.created | 2021-05-06 | |
| dc.date.issued | 2019 | |
| dc.identifier.citation | In: ICAR CONFERENCE, 43., 2019, Prague. Proceedings... Rome: ICAR, 2019. | |
| dc.identifier.uri | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1131717 | - |
| dc.description | Background: Genetic Improvement Programs (GIP) aim to enhance productionefficiency of beef cattle. The main way to guide this enhancement is by choosing thebest mates among sires and cows, in order to maximize the offspring GeneticQualification Index (QGI), which is measured by an index defined by the GIP andcomputed for each animal of the herd. This paper describes a genetic algorithm, whichcan recommend an optimal set of matings among sires and cows, in order to maximizethe QGI of the herd. Breeders can define constraints regarding level of problems,which must be avoided, and they also can alter the traits relative importance consideredin QGI, according their particular interests. This algorithm was applied to a herd of aBrazilian breeder, which participates of a GIP, and it found optimal matings in order toincrease QGI value. We have simulated different scenarios considering variations onfitness functions, which combine QGI and level of problems, in order to find the optimalmatings. Proposed approach was successfully used to recommend optimal matingdecisions by Brazilian Hereford and Braford cattle breeders Association leading to animprovement of offspring QGI.Keywords: Genetic Improvement, Beef Cattle, Artificial Intelligence, EvolutionaryComputing. | |
| dc.language.iso | eng | |
| dc.relation.ispartofseries | (ICAR. Technical series n. 24) | |
| dc.rights | openAccess | eng |
| dc.title | Optimizing mate selection: a genetic algorithm approach. | |
| dc.type | Artigo em anais e proceedings | |
| dc.subject.thesagro | Acasalamento Controlado | |
| dc.subject.thesagro | Análise | |
| dc.subject.thesagro | Gado de Corte | |
| dc.subject.thesagro | Performance | |
| dc.subject.thesagro | Bovino | |
| dc.description.notes | Editors: J. Kucera, P. Bucek, D. Lipovsky, X. Bourrigan and M. Burke. | |
| dc.format.extent2 | p. 54-62. | |
| riaa.ainfo.id | 1131717 | |
| riaa.ainfo.lastupdate | 2021-05-06 | |
| dc.contributor.institution | D. DE CARVALHO NEVES DA FONTOURA, UNIPAMPA; S. DA SILVA CAMARGO, UNIPAMPA; ROBERTO AUGUSTO DE A TORRES JUNIOR, CNPGC; HENRY GOMES DE CARVALHO, CPPSUL; FERNANDO FLORES CARDOSO, CPPSUL. | |
| Aparece nas coleções: | Artigo em anais de congresso (CPPSUL)  | |
Arquivos associados a este item:
| Arquivo | Descrição | Tamanho | Formato | |
|---|---|---|---|---|
| Fontoura-et-al.pdf | 313,09 kB | Adobe PDF |  Visualizar/Abrir |
