The genetic algorithm, as well as the stepwise algorithm, selects MD based on the accuracy of the OLS regression built on this set of MD, while the PLS-VIP and Boruta algorithms select according to criteria that have nothing to do with the accuracy of the OLS regression. Therefore, just as in the case of a step-by-step algorithm, one can expect that the accuracy of the OLS regression built on these MD will be quite high. Indeed, Table shows that the genetic algorithm allows obtaining relatively accurate linear equations. If we compare random pairs of MD sets obtained in different runs, then on average only ~2.2 out of 10 MD are the same for GA. The accuracy of final linear equations for GA is close to that for SEQ_ADD, while the reproducibility of MD selection is significantly worse compared with SEQ_ADD, LASSO and BORUTA methods at least with the used number of generations. Same as for the SEQ_ADD algorithm, the probability to be selected in each repeat is shown in Fig. .