02078naa a2200205 a 450000100080000000500110000800800410001902400520006010000210011224500940013326000090022752014700023665000150170665000130172165000240173465000180175870000190177670000250179577300520182021593542023-12-08       2023    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.55746/treed.2023.03.0012DOI1 aSIMIQUELI, G. F.  aMaximizing multi-trait gain and diversity with genetic algorithms.h[electronic resource]  c2023  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.  aAlgorithms  aGenetics  aSystem optimization  aTree breeding1 aRESENDE, R. T.1 aRESENDE, M. D. V. de  tTreeDimensionalgv. 10, e023001, p. 1-14, 2023.