## 5 Numerical Experiment
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Differential Evolution (DE)** & \multicolumn{2}{c}{**Fish School Search with Exp. Step Decay (ETFSS)**} \\ \hline
**Parameter Title** & **Value** & **Parameter Title** & **Value** \\ \hline \(p_{\text{{\it crossover}}}\) & 0.7 & \(step_{\text{{\it mid}}}\) & 0.7 \\ \(p_{\text{{\it mutation}}}\) & 0.3 & \(step_{\text{{\it pol}}}\) & 0.7 \\ Iteration limit & 30 & Iteration limit & 30 \\ Population size & 20 & Population size & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Parameters of the considered biology-inspired algorithms. Figure 6: Convergence of the considered biology-inspired algorithms DE and ETFSS optimizing the macro f1 score (10) in testing part of the dataset containing program texts solving automatically generated tasks of various types (see Table 1): (**a**) type 8; (**b**) type 9; (**c**) type 10; (**d**) type 11. According to Figure 6 and Table 7, the biology-inspired algorithms with small iteration limit and small agent count show similar performance, and are able to stabilize ELM and improve the accuracy of the resulting model up to 100% in many of the considered multi-class classification problems. At the cost of time losses during the training phase, biology-inspired algorithms successfully automated the development of very accurate and fast ELM-based classifiers. The most accurate ELM-based classifiers evolved with the DE and ETFSS algorithms were incorporated into the DTA system and enabled real-time analysis of program texts submitted by students (see Figure 7b). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Task Type**} & \multirow{2}{*}{**Alg.**} & \multicolumn{4}{c}{**Accuracy on Testing Data, \%**} & \multirow{2}{*}{**Task**} & \multicolumn{4}{c}{**Accuracy on Testing Data, \%**} \\ \cline{3-10}  & & **Accuracy** & **Precision** & **Recall** & **F1** & & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline \multirow{4}{*}{1} & ELM & 100.00 & 100.00 & 99.99 & 99.99 & & 99.89 & 99.54 & 99.25 & 99.34 \\  & DELM & 100.00 & 100.00 & 100.00 & 100.00 & 7 & 100.00 & 100.00 & 100.00 & 100.00 \\  & FELM & 100.00 & 100.00 & 100.00 & 100.00 & & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline \multirow{4}{*}{2} & ELM & 99.82 & 99.72 & 99.82 & 99.77 & & 97.05 & 90.08 & 96.18 & 92.14 \\  & DELM & 99.90 & 99.76 & 99.90 & 99.83 & 8 & 97.56 & **97.80** & 97.15 & **97.45** \\  & FELM & 99.90 & 99.76 & 99.90 & 99.83 & & **97.89** & 97.05 & **97.52** & 97.23 \\ \hline \multirow{4}{*}{3} & ELM & 99.93 & 99.95 & 99.96 & 99.95 & & 99.35 & 98.26 & 98.37 & 98.24 \\  & DELM & 100.00 & 100.00 & 100.00 & 100.00 & 9 & 99.95 & 99.99 & 99.79 & 99.88 \\  & FELM & 100.00 & 100.00 & 100.00 & 100.00 & & **99.95** & **99.99** & **99.79** & **99.88** \\ \hline \multirow{4}{*}{4} & ELM & 99.95 & 99.30 & 99.98 & 99.59 & & 98.23 & 88.95 & 94.62 & 90.75 \\  & DELM & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 10 & **99.34** & 95.90 & **98.99** & **97.26** \\  & FELM & 100.00 & 100.00 & 100.00 & 100.00 & & 99.07 & **96.27** & 97.16 & 96.65 \\ \hline \multirow{4}{*}{5} & ELM & 99.98 & 99.76 & 99.99 & 99.83 & & 98.24 & 95.66 & 97.74 & 96.30 \\  & DELM & 100.00 & 100.00 & 100.00 & 100.00 & 11 & 99.60 & 99.27 & 99.39 & 99.28 \\  & FELM & 100.00 & 100.00 & 100.00 & 100.00 & & **99.60** & **99.27** & **99.75** & **99.47** \\ \hline \multirow{4}{*}{6} & ELM & 98.67 & 91.40 & 96.07 & 92.84 & & & & & \\  & DELM & 98.76 & 92.22 & **98.01** & 94.54 & & & & & \\  & **FELM** & **98.76** & **92.43** & 97.96 & **94.55** & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy comparison of ELM, DELM (tuned with DE), and FELM (tuned with ETFSS). Figure 7: DTA [9; 10] web-based UI: (**a**) displaying an error message, (**b**) displaying real-time program text analysis results obtained from ELM fine-tuned with biology-inspired algorithms.