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Registro Completo |
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
08/12/2023 |
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Data da última atualização: |
08/12/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SIMIQUELI, G. F.; RESENDE, R. T.; RESENDE, M. D. V. de. |
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Afiliação: |
GUILHERME FERREIRA SIMIQUELI, CORTEVA AGRISCIENCE; RAFAEL TASSINARI RESENDE, UNIVERSIDADE FEDERAL DE GOIÁS; MARCOS DEON VILELA DE RESENDE, CNPCa. |
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Título: |
Maximizing multi-trait gain and diversity with genetic algorithms. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
TreeDimensional, v. 10, e023001, p. 1-14, 2023. |
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DOI: |
https://doi.org/10.55746/treed.2023.03.001 |
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Idioma: |
Inglês |
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Conteúdo: |
Genetic gain followed by loss of diversity is not ideal in breeding programs for several species, and most studies face this problem for single traits. Thus, we propose a selection method based on Genetic Algorithms (GA) to optimize the gains for multi-traits that have a low reduction of status number (NS), which takes into account equal contributions from individuals as a result of practical issues in tree breeding. Real data were used to compare GA with a method based on a branch and bound algorithm (BB) for the single-trait problem. Simulated and real data were used to compare GA with a multi-trait method adapted from Mulamba and Mock (MM) (a genotypic ranking approach) through a range of selected individuals’ portions. The GA reached a similar gain and NS in a shorter processing time than BB. This shows the efficacy of GA in solving combinatorial NP-hard problems. In a selected portion of 1% and 2.5%, the GA had low reduction in the overall gain average and greater NS than the MM. In a selection of 20%, the GA reached the same NS as the base population and a greater gain than MM for the simulated data. The GA selected a lower number of individuals than expected at 10% and 20% selection, which contributed to a more practical breeding program that maintained the gains and without the loss of genetic diversity. Thus, GA proved to be a reliable optimization tool for multi-trait scenarios, and it can be effectively applied in tree breeding. |
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Thesaurus Nal: |
Algorithms; Genetics; System optimization; Tree breeding. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1159354/1/Maximizing-multi-trait-gain.pdf
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Marc: |
LEADER 02078naa a2200205 a 4500
001 2159354
005 2023-12-08
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.55746/treed.2023.03.001$2DOI
100 1 $aSIMIQUELI, G. F.
245 $aMaximizing multi-trait gain and diversity with genetic algorithms.$h[electronic resource]
260 $c2023
520 $aGenetic gain followed by loss of diversity is not ideal in breeding programs for several species, and most studies face this problem for single traits. Thus, we propose a selection method based on Genetic Algorithms (GA) to optimize the gains for multi-traits that have a low reduction of status number (NS), which takes into account equal contributions from individuals as a result of practical issues in tree breeding. Real data were used to compare GA with a method based on a branch and bound algorithm (BB) for the single-trait problem. Simulated and real data were used to compare GA with a multi-trait method adapted from Mulamba and Mock (MM) (a genotypic ranking approach) through a range of selected individuals’ portions. The GA reached a similar gain and NS in a shorter processing time than BB. This shows the efficacy of GA in solving combinatorial NP-hard problems. In a selected portion of 1% and 2.5%, the GA had low reduction in the overall gain average and greater NS than the MM. In a selection of 20%, the GA reached the same NS as the base population and a greater gain than MM for the simulated data. The GA selected a lower number of individuals than expected at 10% and 20% selection, which contributed to a more practical breeding program that maintained the gains and without the loss of genetic diversity. Thus, GA proved to be a reliable optimization tool for multi-trait scenarios, and it can be effectively applied in tree breeding.
650 $aAlgorithms
650 $aGenetics
650 $aSystem optimization
650 $aTree breeding
700 1 $aRESENDE, R. T.
700 1 $aRESENDE, M. D. V. de
773 $tTreeDimensional$gv. 10, e023001, p. 1-14, 2023.
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Registro original: |
Embrapa Café (CNPCa) |
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Registro Completo
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Biblioteca(s): |
Embrapa Agrobiologia. |
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Data corrente: |
04/10/2005 |
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Data da última atualização: |
12/01/2010 |
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Autoria: |
OLIVEIRA, F. L. de; GUERRA, J. G. M.; JUNQUEIRA, R. M.; SILVA, E. E. da; SILVA, V. V.; RIBEIRO, R. de L. D.; ALMEIDA, D. L. de. |
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Título: |
Plantio direto do inhame na palhada de aveia preta e cultivo consorciado com Crotalaria juncea. |
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Ano de publicação: |
2005 |
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Fonte/Imprenta: |
Seropédica: Embrapa Agrobiologia, 2005. |
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Páginas: |
4 p. |
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Série: |
(Embrapa Agrobiologia. Comunicado Técnico, 78). |
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ISSN: |
1517-8862 |
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Idioma: |
Português |
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Palavras-Chave: |
Aveia preta; Direct sowing; Zero tillage. |
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Thesagro: |
Alimento Para Animal; Avena Strigosa; Crotalária Juncea; Inhame; Palhada; Plantio Direto; Tubérculo. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/CNPAB-2010/32917/1/cot078.pdf
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Marc: |
LEADER 00930nam a2200325 a 4500
001 1596850
005 2010-01-12
008 2005 bl uuuu u0uu1 u #d
022 $a1517-8862
100 1 $aOLIVEIRA, F. L. de
245 $aPlantio direto do inhame na palhada de aveia preta e cultivo consorciado com Crotalaria juncea.
260 $aSeropédica: Embrapa Agrobiologia$c2005
300 $a4 p.
490 $a(Embrapa Agrobiologia. Comunicado Técnico, 78).
650 $aAlimento Para Animal
650 $aAvena Strigosa
650 $aCrotalária Juncea
650 $aInhame
650 $aPalhada
650 $aPlantio Direto
650 $aTubérculo
653 $aAveia preta
653 $aDirect sowing
653 $aZero tillage
700 1 $aGUERRA, J. G. M.
700 1 $aJUNQUEIRA, R. M.
700 1 $aSILVA, E. E. da
700 1 $aSILVA, V. V.
700 1 $aRIBEIRO, R. de L. D.
700 1 $aALMEIDA, D. L. de
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Embrapa Agrobiologia (CNPAB) |
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