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. 2022 Aug 4;12:13412. doi: 10.1038/s41598-022-17707-w

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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The Genetic Algorithm workflow. The steps are: (1) Generation of the initial population of solutions; (2) Evaluation of fitness values of each solution within the population; (3) The “mating” process of the solution, in which the probability of a solution to be selected is proportional to the estimated fitness value; (4) The random designation of crossover points on each vector of solution during the “mating” process. SC and DC stand for Single- and Double-Crossover, respectively; (5) The introduction of random mutations on the crossover-ed solution vectors; (6) The replacement of the entire population by daughter solutions.