Maximizing multi-trait gain and diversity with Genetic Algorithms
Abstract
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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How to cite
Simiqueli, G. F.; Resende, R. T.; Resende, M. D. V. D. (2023). Maximizing multi-trait gain and diversity with Genetic Algorithms. TreeDimensional Journal, 10(e023001), 1-14. https://doi.org/10.55746/treed.2023.03.001.
@article{simiqueli2023,
title={Maximizing multi-trait gain and diversity with Genetic Algorithms},
author={Simiqueli, Guilherme Ferreira and Resende, Rafael Tassinari and Resende, Marcos Deon Vilela de},
journal={TreeDimensional Journal},
year={2023},
volume={10},
number={e023001},
pages={1-14},
doi={10.55746/treed.2023.03.001}
}TY - JOUR AU - Simiqueli, Guilherme Ferreira AU - Resende, Rafael Tassinari AU - Resende, Marcos Deon Vilela de TI - Maximizing multi-trait gain and diversity with Genetic Algorithms JO - TreeDimensional Journal PY - 2023 VL - 10 IS - e023001 SP - 1 EP - 14 DO - 10.55746/treed.2023.03.001 AB - 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. KW - Optimization KW - Tree breeding KW - Heuristic index KW - Status number KW - Evolutionary algorithm KW - Artificial intelligence ER -
Simiqueli, G. F.; Resende, R. T.; Resende, M. D. V. D. (2023). Maximizing multi-trait gain and diversity with Genetic Algorithms. TreeDimensional Journal, 10(e023001), 1-14. https://doi.org/10.55746/treed.2023.03.001. Import via Mendeley Web Importer using DOI 10.55746/treed.2023.03.001
Add to Zotero using DOI 10.55746/treed.2023.03.001 Simiqueli, G. F.; Resende, R. T.; Resende, M. D. V. D. (2023). Maximizing multi-trait gain and diversity with Genetic Algorithms. TreeDimensional Journal, 10(e023001), 1-14. https://doi.org/10.55746/treed.2023.03.001.
Simiqueli, G. F.; Resende, R. T.; Resende, M. D. V. D. (2023). Maximizing multi-trait gain and diversity with Genetic Algorithms. TreeDimensional Journal, 10(e023001), 1-14. https://doi.org/10.55746/treed.2023.03.001.