Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1159354
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dc.contributor.authorSIMIQUELI, G. F.
dc.contributor.authorRESENDE, R. T.
dc.contributor.authorRESENDE, M. D. V. de
dc.date.accessioned2023-12-08T13:32:13Z-
dc.date.available2023-12-08T13:32:13Z-
dc.date.created2023-12-08
dc.date.issued2023
dc.identifier.citationTreeDimensional, v. 10, e023001, p. 1-14, 2023.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1159354-
dc.descriptionGenetic 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.
dc.language.isoeng
dc.rightsopenAccess
dc.titleMaximizing multi-trait gain and diversity with genetic algorithms.
dc.typeArtigo de periódico
dc.subject.nalthesaurusSystem optimization
dc.subject.nalthesaurusTree breeding
dc.subject.nalthesaurusAlgorithms
dc.subject.nalthesaurusGenetics
riaa.ainfo.id1159354
riaa.ainfo.lastupdate2023-12-08
dc.identifier.doihttps://doi.org/10.55746/treed.2023.03.001
dc.contributor.institutionGUILHERME FERREIRA SIMIQUELI, CORTEVA AGRISCIENCE; RAFAEL TASSINARI RESENDE, UNIVERSIDADE FEDERAL DE GOIÁS; MARCOS DEON VILELA DE RESENDE, CNPCa.
Aparece nas coleções:Artigo em periódico indexado (SAPC)

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