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Registro Completo |
Biblioteca(s): |
Embrapa Roraima. |
Data corrente: |
04/12/2012 |
Data da última atualização: |
25/10/2018 |
Tipo da produção científica: |
Artigo em Anais de Congresso |
Autoria: |
VALE, H. S. do; DUARTE, O. R.; CARVALHO, A. dos S.; SILVA, M. R. da; PASSOS, M. A. B. |
Afiliação: |
OTONIEL RIBEIRO DUARTE, CPAF-RR. |
Título: |
Floração e frutificação do tachi (melicoccus bijugatus jacq.) Em Boa Vista, Roraima. |
Ano de publicação: |
2012 |
Fonte/Imprenta: |
In: CONGRESSO BRASILEIRO DE RECURSOS GENÉTICOS, 2., 2012, Belém, PA. Anais... [S.l.]: Sociedade Brasileira de Recursos Genéticos, 2012. |
Idioma: |
Português |
Palavras-Chave: |
Fruteira nativa. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/71423/1/515.doc
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Marc: |
LEADER 00575nam a2200157 a 4500 001 1941263 005 2018-10-25 008 2012 bl uuuu u00u1 u #d 100 1 $aVALE, H. S. do 245 $aFloração e frutificação do tachi (melicoccus bijugatus jacq.) Em Boa Vista, Roraima. 260 $aIn: CONGRESSO BRASILEIRO DE RECURSOS GENÉTICOS, 2., 2012, Belém, PA. Anais... [S.l.]: Sociedade Brasileira de Recursos Genéticos$c2012 653 $aFruteira nativa 700 1 $aDUARTE, O. R. 700 1 $aCARVALHO, A. dos S. 700 1 $aSILVA, M. R. da 700 1 $aPASSOS, M. A. B.
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Embrapa Roraima (CPAF-RR) |
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Registro Completo
Biblioteca(s): |
Embrapa Pecuária Sul. |
Data corrente: |
06/05/2021 |
Data da última atualização: |
06/05/2021 |
Tipo da produção científica: |
Artigo em Anais de Congresso |
Autoria: |
FONTOURA, D. de C. N. da; CAMARGO, S. da S.; TORRES JUNIOR, R. A. de A.; CARVALHO, H. G. de; CARDOSO, F. F. |
Afiliação: |
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. |
Título: |
Optimizing mate selection: a genetic algorithm approach. |
Ano de publicação: |
2019 |
Fonte/Imprenta: |
In: ICAR CONFERENCE, 43., 2019, Prague. Proceedings... Rome: ICAR, 2019. |
Páginas: |
p. 54-62. |
Série: |
(ICAR. Technical series n. 24) |
Idioma: |
Inglês |
Notas: |
Editors: J. Kucera, P. Bucek, D. Lipovsky, X. Bourrigan and M. Burke. |
Conteúdo: |
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. |
Thesagro: |
Acasalamento Controlado; Análise; Bovino; Gado de Corte; Performance. |
Categoria do assunto: |
G Melhoramento Genético |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/223096/1/Fontoura-et-al.pdf
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Marc: |
LEADER 02086nam a2200253 a 4500 001 2131717 005 2021-05-06 008 2019 bl uuuu u00u1 u #d 100 1 $aFONTOURA, D. de C. N. da 245 $aOptimizing mate selection$ba genetic algorithm approach.$h[electronic resource] 260 $aIn: ICAR CONFERENCE, 43., 2019, Prague. Proceedings... Rome: ICAR$c2019 300 $ap. 54-62. 490 $a(ICAR. Technical series n. 24) 500 $aEditors: J. Kucera, P. Bucek, D. Lipovsky, X. Bourrigan and M. Burke. 520 $aBackground: 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. 650 $aAcasalamento Controlado 650 $aAnálise 650 $aBovino 650 $aGado de Corte 650 $aPerformance 700 1 $aCAMARGO, S. da S. 700 1 $aTORRES JUNIOR, R. A. de A. 700 1 $aCARVALHO, H. G. de 700 1 $aCARDOSO, F. F.
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