Data/hora: 06/06/2024 12:06:46

Biblioteca(s): 	Embrapa Pecuária Sul.	
Tipo da produção científica: 	Artigo em Anais de Congresso	
Data corrente: 	06/05/2021	
Data da última atualização: 	06/05/2021	
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: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/223096/1/Fontoura-et-al.pdf	
Registro original: 	Embrapa Pecuária Sul (CPPSUL)	
Exemplares: 	CPPSUL - (14799-1) - UPC - AA - DD -  -  -  - DIS