Publications

2025

• Conference paper: Pagliuca, P., Zribi, M., Tufo, G. & Pitolli, F. (2025). The Trade-off between Efficiency, Sustainability and Explainability: a Comparative Study on the Quality Control of Laboratory Consumables. In Proceedings of the 2025 International Joint Conference on Neural Networks (IJCNN 2025). DOI: https://doi.org/10.1109/IJCNN64981.2025.11227917

• Conference paper: Pagliuca, P., & Vitanza, A. (2025). Learning Locomotion by Co-Evolution of Morphological and Neural Parameters. In Proceedings of the 2025 IEEE International Conference on Development and Learning (ICDL 2025). DOI: https://doi.org/10.1109/ICDL63968.2025.11204450

• Journal paper: Pagliuca, P. (2025) Discovering a Single Neural Network Controller for Multiple Tasks with Evolutionary Algorithms. Journal of Artificial Intelligence and Autonomous Intelligence, 2 (2), pp. 322-348. DOI: https://doi.org/10.54364/JAIAI.2024.1120

• Conference paper: Pagliuca, P., Trivisano, G. & Vitanza, A. (2025). How to Evolve Aggregation in Robotic Multi-Agent Systems. In Proceedings of the 26th Workshop From Objects to Agents (WOA 2025), vol. 4028, pp. 140-156.

• Conference paper: Pagliuca, P., Nolfi, S. & Vitanza, A. (2025). Evorobotpy3: a flexible and easy-to-use simulation tool for Evolutionary Robotics. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO 2025 Companion), pp. 155-158. DOI: https://doi.org/10.1145/3712255.3726545

• Conference paper: Pagliuca, P., Favia, M., Livi. S. & Vitanza, A. (2025). Conceptualizing Evolving Interdependence in Groups: Insights from the Analysis of Two-Agent Systems. In Proceedings of the 2025 21st International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT 2025), pp. 828-833. DOI: https://doi.org/10.1109/DCOSS-IoT65416.2025.00126

• Journal paper: Pagliuca, P., Favia, M., Livi. S. & Vitanza, A. (2025) Interdipendenza nei gruppi: esperimenti con robot sociali. Sistemi intelligenti, Rivista quadrimestrale di scienze cognitive e di intelligenza artificiale, 37 (2), pp. 335-355. DOI: https://doi.org/10.1422/117539

• Book chapter: Pagliuca, P., & Inglese, D. Y. (2025). The Importance of Functionality over Complexity: A Preliminary Study on Feed-Forward Neural Networks. In Advanced Neural Artificial Intelligence: Theories and Applications (pp. 447-458). Singapore: Springer Nature Singapore. DOI: https://doi.org/10.1007/978-981-96-0994-9_41

• Journal paper: Pagliuca, P. & Vitanza, A. (2025) A Comparative Study of Evolutionary Strategies for Aggregation Tasks in Robot Swarms: Macro- and Micro-Level Behavioral Analysis. IEEE Access, 13, pp. 72721-72735. DOI: https://doi.org/10.1109/ACCESS.2025.3554344

• Journal paper: Nolfi, S. & Pagliuca, P. (2025) Global Progress in Competitive Co-Evolution: a Systematic Comparison of Alternative Methods. Frontiers in Robotics and AI, 11. DOI: https://doi.org/10.3389/frobt.2024.1470886

2024

• Journal paper: Pagliuca, P. (2024) Analysis of the Exploration-Exploitation Dilemma in Neutral Problems with Evolutionary Algorithms. Journal of Artificial Intelligence and Autonomous Intelligence, 1 (2), pp. 110-121. DOI: https://doi.org/10.54364/JAIAI.2024.1108

• Journal paper: Vitanza, A., Morleo, F., & Pagliuca, P. (2024) Interazione Uomo-Robot: l’importanza degli aspetti psicologici nella cura degli anziani. Sistemi intelligenti, Rivista quadrimestrale di scienze cognitive e di intelligenza artificiale, 36 (3), pp. 631-640. DOI: https://doi.org/10.1422/115335

• Journal paper: Pagliuca, P. (2024). Learning and Evolution: Factors Influencing an Effective Combination. AI, 5(4), pp. 2393-2432. DOI: https://doi.org/10.3390/ai5040118

• Conference paper: Zribi, M., Pagliuca, P. & Pitolli F. (2024). Enhancing Industrial Quality Control Efficiency: an Innovative Deep Learning Approach for Sustainable Process Monitoring.  In Proceedings of the 9th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2024). DOI: https://doi.org./10.23967/eccomas.2024.226

• Conference paper: Vitanza, A., Morleo, F., & Pagliuca, P. (2024). Can Educational Robotics Experiences Shape Early Childhood Interpersonal Dynamics? An Exploratory Investigation. In 2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) (pp. 873-878). IEEE. DOI: https://doi.org/10.1109/MetroXRAINE62247.2024.10796231

• Conference paper: Tufo, G., Zribi, M., Pagliuca, P., & Pitolli, F. (2024). An Explainable Convolutional Neural Network for the Detection of Drug Abuse. In Proceedings of the First Workshop on Explainable Artificial Intelligence for the medical domain (EXPLIMED 2024), vol. 3831.

• Journal paper: Zribi, M., Pagliuca, P., & Pitolli, F. (2024). A Computer Vision-Based Quality Assessment Technique for the automatic control of consumables for analytical laboratories. Expert Systems with Applications, vol. 256. DOI: https://doi.org/10.1016/j.eswa.2024.124892

• Conference paper: Pagliuca, P., & Vitanza, A. (2024). The role of n in the n-mates evaluation method: a quantitative analysis. In Proceedings of the 2024 Artificial Life Conference (ALIFE 2024), MIT press, pp. 812-814. DOI: https://doi.org/10.1162/isal_a_00824

• Conference paper: Pagliuca, P., & Vitanza, A. (2024). Enhancing Aggregation in Locomotor Multi-Agent Systems: a Theoretical Framework. In Proceedings of the 25th Workshop From Objects to Agents (WOA 2024), vol. 3735, pp. 42-57.

2023

• Conference paper: Pagliuca, P., & Vitanza, A. (2023). N-Mates Evaluation: a New Method to Improve the Performance of Genetic Algorithms in Heterogeneous Multi-Agent Systems. In Proceedings of the 24th Workshop From Objects to Agents (WOA 2023), vol. 3579, pp. 123-137.

• Conference paper: Vitanza, A., Pagliuca, P., Cantucci, F., & Nolfi, S. (2023). Skeleton Timed Up and Go on MARIO robot. In 2023 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (IEEE MetroXRAINE 2023) (pp. 1171-1176). IEEE. DOI: https://doi.org/10.1109/MetroXRAINE58569.2023.10405769

• Book chapter: Pagliuca, P., & Vitanza, A. (2023). Evolving aggregation behaviors in swarms from an evolutionary algorithms point of view. In Applications of Artificial Intelligence and Neural Systems to Data Science (pp. 317-328). Singapore: Springer Nature Singapore. DOI: https://doi.org/10.1007/978-981-99-3592-5_30

• Journal paper: Pagliuca, P., Inglese, D. Y., & Vitanza, A. (2023). Measuring emergent behaviors in a mixed competitive-cooperative environment. International Journal of Computer Information Systems and Industrial Management Applications, vol. 15, pp. 69-86.

2022

• Conference paper: Pagliuca, P., & Vitanza, A. (2022). Self-organized Aggregation in Group of Robots with OpenAI-ES. In International Conference on Soft Computing and Pattern Recognition (SoCPaR 2022) (pp. 770-780). Cham: Springer Nature Switzerland. DOI: https://doi.org/10.1007/978-3-031-27524-1_75

• Conference paper: Pagliuca, P., Milano, N., & Nolfi, S. (2022). Automated Categorization of Behavioral Quality Through Deep Neural Networks. In 2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (IEEE MetroXRAINE 2022) (pp. 372-376). IEEE. DOI: https://doi.org/10.1109/MetroXRAINE54828.2022.9967505

2021

• Journal paper: Pagliuca, P., & Nolfi, S. (2022). The dynamic of body and brain co-evolution. Adaptive Behavior, 30(3), pp. 245-255. DOI: https://doi.org/10.1177/1059712321994685 (published online in 2021)

2020

• Journal paper: Pagliuca, P., Milano, N., & Nolfi, S. (2020). Efficacy of modern neuro-evolutionary strategies for continuous control optimization. Frontiers in Robotics and AI, 7, 98. DOI: https://doi.org/10.3389/frobt.2020.00098

2019

• Journal paper: Pagliuca, P., & Nolfi, S. (2019). Robust optimization through neuroevolution. PLOS ONE, 14(3), e0213193. DOI: https://doi.org/10.1371/journal.pone.0213193

• Journal paper: Milano, N., Pagliuca, P., & Nolfi, S. (2019). Robustness, evolvability and phenotypic complexity: insights from evolving digital circuits. Evolutionary Intelligence, 12, pp. 83-95. DOI: https://doi.org/10.1177/1059712315608424

2018

• Journal paper: Pagliuca, P., Milano, N., & Nolfi, S. (2018). Maximizing adaptive power in neuroevolution. PLOS ONE, 13(7), e0198788. DOI: https://doi.org/10.1371/journal.pone.0198788

2015

• Journal paper: Pagliuca, P., & Nolfi, S. (2015). Integrating learning by experience and demonstration in autonomous robots. Adaptive Behavior, 23(5), pp. 300-314. DOI: https://doi.org/10.1177/1059712315608424