## 2 Materials and Methods
### Adaptive Selection of Algorithm Parameters
The experiments showed a significant improvement in the results compared to those obtained earlier without the evolutionary selection of coefficients (best and average results were determined by 40 runs), which is reflected in Table 2. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Problem** & \(I_{z}\) & \(L_{min}\) & \(L_{agg}\) & \(Iaco\) & \(C_{ant}\) & \(\alpha\) & \(\beta\) & \(\rho\) & \(\gamma\) & \(\lambda\) \\ \hline abz6 & 40 & 948 & 977.333 & 1000 & 100 & 0.63 & 2.0 & 0.696 & 28 & 1.3 \\ abz6 & 40 & 945 & 982.524 & 1000 & 100 & 1.1 & 1.0 & 0.499 & 900 & 1.3 \\ ft10 & 40 & 950 & 995.866 & 1000 & 100 & 0.63 & 1.2 & 0.7 & 1000 & 1.1 \\ ft10 & 20 & 951 & 1006.1 & 1000 & 50 & 0.3781 & 1.711 & 0.97 & 1108.7 & 2.277 \\ la17 & 20 & 784 & 805.4 & 1000 & 100 & 0.392 & 2.7701 & 0.2998 & 425.5387 & 1.9615 \\ la17 & 20 & 785 & 798.25 & 1000 & 100 & 0.0341 & 1.7968 & 0.5461 & 949.1535 & 4.5589 \\ la15 & 20 & 1207 & 1215.45 & 1000 & 100 & 0.531 & 1.72 & 0.663 & 1032 & 1.2 \\
3\_Plates & 10 & 657.55 & 662.075 & 30 & 10 & 0.2782 & 0.4251 & 0.4919 & 296.61 & 2.5373 \\
3\_Plates & 10 & 657.55 & 662.3275 & 30 & 10 & 0.1754 & 0.5705 & 0.3836 & 326.05 & 2.5083 \\ la01 & 10 & 666 & 670.2 & 200 & 20 & 0.2262 & 0.8665 & 0.6883 & 903.3459 & 2.0363 \\ la01 & 10 & 666 & 669.4 & 200 & 20 & 0.3542 & 0.6527 & 0.8001 & 120.8012 & 2.1305 \\ la21 & 10 & 1107 & 1150.9 & 2000 & 100 & 0.7478 & 1.1134 & 0.3488 & 790.57 & 2.2236 \\
6\_Plates & 20 & 107 & 111.45 & 30 & 10 & 0.6218 & 2.7953 & 0.6995 & 335.6241 & 1.2376 \\
6\_Plates & 20 & 107 & 111.5 & 30 & 10 & 0.6204 & 0.2863 & 0.0775 & 137.6017 & 1.479 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Examples of the best sets of coefficients obtained using the genetic algorithm. In Table 2, in addition to those already described, the following notations are used:

* \(L_{m1}\) is the best solution that was recorded before using the GA (the coefficients were selected manually);
* \(L_{a1}\) is the average value of the solutions that were recorded before using the GA;
* \(L_{g}\) is the best solution that was obtained using the GA;
* \(L_{ga}\) is the average value of the solutions that were obtained using the coefficients found by the GA;
* \(L_{m2}\) is the best solution that was obtained using the coefficients found by averaging over other solved problems;
* \(L_{a2}\) is the average value of the solutions that were obtained using the coefficients found by averaging over other solved problems (except for the coefficient \(\gamma\)). Here, averaged coefficients are understood as a set of coefficients found as the arithmetic means among the best-found coefficients for problems of similar dimensions. To assess the improvement in the quality of schedules compiled with the adaptation of the method parameters, quasi-optimal solutions to test problems were used [52]. The results are shown in Table 3. For the problem of processing plates, the result 657.55 [51] is given, which also coincides with the solution obtained in this paper. The experiments showed a significant improvement in the results (up to 12%) compared to those found earlier (without the adaptation of the coefficients). The solutions obtained by the proposed adaptive algorithm for many problems from [47; 48; 49] turned out to be no worse than the known quasi-optimal solutions for these problems. The deviation from the known quasi-optimal solutions does not exceed 6% (in the work of the founders of the ACO algorithm [27], they state the 10% deviation in their results to job-shop problems). At the same time, the adaptive method makes it possible to obtain guaranteed solutions of the specified quality at each run with enough iterations.