Maximizing multi-trait gain and diversity with genetic algorithms.
| dc.contributor | GUILHERME FERREIRA SIMIQUELI, CORTEVA AGRISCIENCE; RAFAEL TASSINARI RESENDE, UNIVERSIDADE FEDERAL DE GOIÁS; MARCOS DEON VILELA DE RESENDE, CNPCa. | |
| dc.creator | SIMIQUELI, G. F. | |
| dc.creator | RESENDE, R. T. | |
| dc.creator | RESENDE, M. D. V. de | |
| dc.date | 2023-12-08T13:32:13Z | |
| dc.date | 2023-12-08T13:32:13Z | |
| dc.date | 2023-12-08 | |
| dc.date | 2023 | |
| dc.date.accessioned | 2026-07-01T00:27:16Z | |
| dc.description | 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. | |
| dc.identifier | TreeDimensional, v. 10, e023001, p. 1-14, 2023. | |
| dc.identifier | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1159354 | |
| dc.identifier | https://doi.org/10.55746/treed.2023.03.001 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/404432 | |
| dc.language | eng | |
| dc.rights | openAccess | |
| dc.subject | System optimization | |
| dc.subject | Tree breeding | |
| dc.subject | Algorithms | |
| dc.subject | Genetics | |
| dc.title | Maximizing multi-trait gain and diversity with genetic algorithms. | |
| dc.type | Artigo de periódico |