## 3 Results and Discussion  
For the development of this work, we used an HP ENVY Laptop 13-ad0 xx, equipped with an Intel Core i7-7500U CPU @ 2.70 GHz (2.094 GHz), 8 GB DDR3 SDRAM, and running Microsoft Windows 10 Home Single Language, version 10.0.18363. For the programming of this research, we used C# on Visual Studio Community version 2022 and Python 3.13.1 on PyCharm Community Edition 2024.3.1. To begin, the initial structure of the Artificial Neural Network (ANN) was developed using the following parameters: learning rate of 0.3, momentum of 0.2, and 500 iterations. The basic architecture consists of an input layer with \(x\) neurons, where \(x\) represents the number of attributes in the dataset, a hidden layer with the same number of neurons as the input layer, and an output layer with \(y\) neurons, where \(y\) corresponds to the number of classes in the dataset (Figure 4). The preprocessed datasets were used to train the ANN with a backpropagation algorithm, which optimizes the network's weights by minimizing error through gradient descent. Additionally, we evaluate the performance of the algorithm with a 10-fold cross-validation approach to reduce the dependence on the initial data partitioning. The preliminary results for the initial ANN structure are presented in Table 3, where the first column lists the tested datasets and the second column reports the accuracy achieved. For Artificial Neural Network evolution process, we developed a genetic algorithm with the operators designed (Genotype Mutation Operator and Differential Crossover Operator) based on the genetic encoding structure of NEAT. The evolutive process includes three hyperparameters that modify its behavior. First, we analyzed the population size, considering that each population element represents a genotype of the initial neural network. Additionally, we determined the number of generations for the genetic algorithm's evolution and defined the number of training iterations for ANN learning. Based on the preliminary experiment results, the value ranges shown in Table 4 were established for the hyperparameters. Figure 4: Initial structure for Artificial Neural Network. \begin{table}
\begin{tabular}{l c} \hline \hline
**Dataset** & **Accuracy** \\ \hline banknote\_auth & 57.20\% \\ divorce & 98.24\% \\ iris & 96.67\% \\ pulsar\_stars & 92.64\% \\ car\_evaluation & 91.38\% \\ contact-lenses & 79.17\% \\ weather.nominal & 71.43\% \\ diabetes & 74.84\% \\ weather.numeric & 64.29\% \\ QSAR Bioconcentration & 61.87\% \\ seeds & 59.52\% \\ cryotherapy & 53.33\% \\ Heart\_disease & 49.18\% \\ glass & 35.05\% \\ abalone & 14.29\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy obtained with proposed initial ANN structure. To define the parameters of the genetic algorithm, we used Grid Search to explore the hyperparameter space within the ranges specified in Table 4. Table 5 presents the test results, highlighting the configurations that achieved an accuracy above 80%. We determined other parameters through Grid Search, selecting a mutation probability from a tested range of 10% to 100% in increments of 10%. The final choice was a 30% mutation probability. For the modification of the total number of weights \(w\) in the network genotype, we used Random Search within a range of 1% to 40%, ensuring that the network structure was not significantly altered. Similarly, for the differential crossover operator, we used Random Search with a range of 0 to 1 and obtained a scale factor \(v\) of 0.7. To evaluate the performance of the proposed genome, we used the parameters that achieved the highest accuracy. Based on the previous table, we selected the configuration from test 8. The accuracy of the algorithms evaluated is presented in Table 6. The first column corresponds to the dataset name. The second column shows the performance of the initially proposed genome. To assess the effectiveness of the evolutionary approach, we compared our proposed model against the NEAT Python package, as shown in the third column.