## 3 Results and Discussion
We developed the NEAT model using Python 3.13.1 and the following libraries: TensorFlow-aarch64 1.2, Scikit-learn 1.6.1, Keras 3.8.0, NumPy 2.2.3, Pandas 2.2.0, and Neat-python 0.92. Finally, the fourth column presents the results obtained with the proposed Cellular-NEAT. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Dataset** & **ANN Basic** & **NEAT Python** & **Cellular-NEAT** \\ \hline banknote\_auth & 57.20\% & 55.93\% & 59.30\% \\ divorce & 98.24\% & 98.50\% & 98.12\% \\ iris & 96.67\% & 98.0\% & 98.0\% \\ pulsar\_stars & 92.64\% & 89.17\% & 89.31\% \\ car\_evaluation & 91.38\% & 94.6\% & 96.0\% \\ contact-lenses & 79.17\% & 82.85\% & 83.76\% \\ weather\_nominal & 71.43\% & 77.8\% & 84.10\% \\ diabetes & 74.84\% & 85.10\% & 89.20\% \\ weather\_numeric & 64.29\% & 66.67\% & 69.34\% \\ QSAR Bioconcentration & 61.87\% & 65.37\% & 67.10\% \\ seeds & 59.52\% & 64.40\% & 66.20\% \\ cryotherapy & 53.33\% & 51.84\% & 53.30\% \\ Heart\_disease & 49.18\% & 60.02\% & 59.70\% \\ glass & 35.05\% & 69.0\% & 68.0\% \\ abalone & 14.29\% & 21.18\% & 20.40\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results obtained via evolutive process. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value Range** \\ \hline Population size & 10–100 \\ Generations & 10–500 \\ Training iterations & 100–1000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Value range for evolutive hyperparameters. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Test** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Population size & 100 & 10 & 10 & 100 & 100 & 10 & 100 & 10 & 10 \\ Generations & 10 & 10 & 10 & 100 & 100 & 100 & 10 & 100 & 100 \\ Training iterations & 100 & 100 & 100 & 100 & 100 & 500 & 500 & 500 & 500 \\ Accuracy & 87 & 83 & 86 & 85 & 86 & 86 & 82 & 89 & 88 & 82 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameter tests for genotype operator. Figure 5 presents the accuracy results to compare the performance of the tested models. The results indicate that the Cellular-NEAT model consistently outperforms the Initial ANN across all datasets. While NEAT Python and Cellular-NEAT exhibit similar performance trends, Cellular-NEAT generally achieves slightly higher accuracy. The initial phenotype was used as the basis for the cellular processing algorithm (PCELL) to expand the solution space and improve the algorithm's performance. At this stage, we employed six processing cells, each receiving the initial phenotype as a parameter. Independently, each cell modified the phenotype by adjusting the number of neurons or the number of layers, as illustrated in Figure 6.  
Table 7 presents an example of the phenotype modification process for the diabetes dataset, showing the accuracy obtained with Cellular-NEAT using the initial genome. It also shows the results of structural modifications in the PCELL processing cells, allowing us to identify the configuration with the best performance. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Solution** & **Structure** & **Cellular-NEAT** \\ \hline initial & 8,7,2,2 & 87.80\% \\ cell 1 & 8,7,2 & 86.19\% \\ cell 2 & 8,7,1 & 85.93\% \\ cell 3 & 8,7,2,2 & 87.63\% \\ cell 4 & 8,7,7,6,2 & 84.30\% \\ cell 5 & 8,6,2,2 & 87.23\% \\ cell 6 & 8,6,2,2 & 89.20\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Modified genome structure and accuracy obtained with PCELL algorithm integration. Figure 5: Accuracy comparison of initial ANN, NEAT Python, and Cellular-NEAT proposed algorithms. Finally, Table 8 shows the accuracy obtained by integrating the PCELL algorithm into the proposed model. The results indicate that, for most of the tested datasets, performance improves when the phenotype is modified during experimentation. We applied a Friedman test to evaluate the performance differences among the three evaluated algorithms: NEAT-Python, Cellular-NEAT, and Cellular-NEAT with PCELL.