## 4 Results
### Assessment-Test Generation (TG)
#### 4.3.2 TG Using Genetic Structures
This setup ensures the estimation of a regression model capable of predicting runtime based on the specified inputs. The plots related to the dependency of N, K and NG on the runtime are shown in Figure 9(a)-c.  
The estimated function is a linear regression that models the relationship between the explanatory variables (N, K, NG) and the algorithm's runtime (Runtime). The intercept coefficient (\(-\)0.19403) represents the estimated runtime when all other variables are zero. The coefficient for N (0.000921372) suggests that for every unit increase in N, the runtime  
Figure 10: The influence of each parameter (N, K and NG) on the runtime of the algorithm: (**a**) N (**b**) K (**c**) NG (**d**) individual correlation. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**N** & **K** & **NG** & **Runtime (s)** & **Runtime for Previous Algorithm (s)** \\ \hline
1000 & 20 & 500 & 6.655214961 & 8.098882903 \\ \hline
2000 & & & 6.682372113 & 8.346786351 \\ \hline
3000 & & & 6.869802016 & 8.200428548 \\ \hline
4000 & & & 6.568096433 & 8.857499464 \\ \hline
5000 & & & 6.759531288 & 9.293001839 \\ \hline
2000 & 30 & 500 & 7.124899292 & 10.177044323 \\ \cline{2-4}  & 50 & & 7.311507564 & 9.689560915 \\ \cline{2-4}  & 70 & & 7.738539504 & 12.886415575 \\ \cline{2-4}  & 90 & & 8.295781972 & 12.904098568 \\ \cline{2-4}  & 100 & & 8.626971045 & 12.422781658 \\ \hline
2000 & 20 & 600 & 8.002793317 & 10.171294628 \\ \cline{2-4}  & & 800 & 10.636422365 & 13.425690146 \\ \cline{2-4}  & & 1000 & 13.094546275 & 16.932125198 \\ \cline{2-4}  & & 1200 & 15.762861716 & 19.769909546 \\ \cline{2-4}  & & 1400 & 18.808763568 & 23.090812005 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Runtime data after the genetic-based implementation. increases by approximately 0.00092 s. The coefficient for K (1.87163 \(\times\) 10\({}^{-5}\)) is much smaller, indicating a minor influence on the runtime compared to N and NG, with a very small increase in runtime for each unit increase in K. In contrast, the coefficient for NG (0.0118544) indicates a significant influence, suggesting that an increase in the value of NG has a much larger impact on runtime, with an increase of approximately 0.01185 s for each unit added in NG. Thus, it can be concluded that NG has the largest impact on performance (runtime), followed by N, and K has a smaller influence. The indicated Pearson coefficients (Figure 10d) suggest different relationships between the independent variables and the execution time. The very strong and positive correlation (+0.986) between NG (number of generations) and Runtime suggests that as NG increases, the execution time tends to increase significantly. The moderately positive relationship (+0.500) between N (number of items) and Runtime indicates a less strong, but still significant, influence on the increase in execution time as N increases. In contrast, the negative correlation (\(-\)0.402) between K (number of items per test) and Runtime suggests that as K increases, the execution time decreases, indicating a possible increased efficiency in the case of tests with more items. Considering the hardware system used, the results indicate that the genetic algorithm can work efficiently even on a less powerful platform, but the complexity can significantly affect the performance on large datasets or for long-running executions. In such cases, further optimization of the algorithm or more powerful hardware may be required to avoid an excessive increase in runtime. Therefore, finding an optimal balance between the algorithm parameters, its complexity and the available hardware resources is essential to obtain quality solutions in a reasonable time. The results show that the most influential parameter of the genetic algorithm for item generation is the number of generations. The significant increase is offset by the smoother results obtained after running a larger number of generations.