## 4 Conclusions  
This research introduced a hybrid neuroevolutionary approach that integrates a genetic algorithm with a NEAT-based structure and a Cellular Processing Algorithm (PCELL) to enhance Artificial Neural Network (ANN) training. The results demonstrated that modifying the phenotypic structure through PCELL improves classification accuracy across multiple datasets. By leveraging evolutionary strategies, the proposed method effectively overcomes the limitations of conventional backpropagation-based training, particularly in avoiding local optima and optimizing network architecture dynamically. The findings confirm that combining cellular processing with evolutionary optimization significantly enhances ANN performance.  
Furthermore, the experimental analysis revealed that the PCELL algorithm contributes to structural refinement, allowing the ANN to adapt more efficiently to different classification tasks. The results indicate that Cellular-NEAT achieves performance comparable to evolutive and neuroevolutionary models while maintaining computational efficiency. The observed improvements suggest that incorporating phenotype-level modifications during training can lead to more adaptable and robust neural networks.  
Future research could explore extending this approach by integrating additional evolutionary mechanisms, such as differential evolution or swarm intelligence, to optimize network architectures further. Additionally, investigating its applicability to deep learning models and real-world datasets with high-dimensional features could provide valuable insights into its scalability. Finally, enhancing the PCELL algorithm with reinforcement learning techniques may lead to more adaptive and autonomous learning strategies, further advancing the field of neuroevolutionary computation. Since this study aimed to evaluate the evolution of fixed-structure neural networks against solutions adapted for dynamic topologies, we conducted tests using evolutionary strategies. However, these were not included in the final analysis, as the results showed a better average on NEAT approach accuracy when compared with those obtained with the basic neural network (see Table 6).  
**Author Contributions:** Conceptualization, H.A.H.-M. and L.A.H.-B.; methodology, A.H.G.-R.; software, A.H.G.-R. and H.A.H.-M.; validation, L.A.H.-B., H.A.H.-M. and A.H.G.-R.; formal analysis, R.P.-R. and K.E.L.-G.; investigation, L.J.G.-d.-A.; resources, R.P.-R.; data curation, L.A.H.-B.; writing--original draft preparation, L.A.H.-B.; writing--review and editing, L.A.H.-B. and A.H.G.-R.; visualization, K.E.L.-G.; supervision, L.J.G.-d.-A.; project administration, L.A.H.-B., A.H.G.-R. and L.J.G.-d.-A.; funding acquisition, R.P.-R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding. The APC was funded by Autonomous University of Tamaulipas.  
**Data Availability Statement:** The benchmark instances for this study are available at [[https://github.com/AnBarajas/Cellular-NEAT.git](https://github.com/AnBarajas/Cellular-NEAT.git)]([https://github.com/AnBarajas/Cellular-NEAT.git](https://github.com/AnBarajas/Cellular-NEAT.git)), accessed on 16 February 2025.  
**Acknowledgments:** A.H.G.-R. would like to acknowledge SECIHTI Mexico for the SNII salary award.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.