Publications

2026

• Journal paper: Pagliuca, P. (2026). Enhancing Performance of Evolutionary Strategies with Symmetric Sampling (Furthermore, Weight Decay). Algorithms, 19(7), 504. DOI: https://doi.org/10.3390/a19070504

• Book chapter: Pagliuca, P. (2026). Learning Enhances Evolutionary Algorithms in Simple Autonomous Agents: A Quantitative and Qualitative Analysis. In Socially Aware and Responsible AI Applications (pp. 331-343). Singapore: Springer Nature Singapore. DOI: https://doi.org/10.1007/978-981-95-4076-1_26

• Journal paper: Pagliuca, P. (2026). Leveraging High Morphology Rates in Body-Brain Co-Evolution: A Case Study on 2D Robot Locomotion. Advances in Knowledge-Based Systems, Data Science and Cybersecurity, 3(2), pp. 533-555. DOI: https://doi.org/10.54364/cybersecurityjournal.2026.3227

• Journal paper: Pagliuca, P. (2026). Whether, How and When Modern Evolutionary Strategies Can Be Improved. Journal of Artificial Intelligence and Autonomous Intelligence, 2(3), pp. 425-449. DOI: https://doi.org/10.54364/JAIAI.2024.1125

• Conference paper: Pagliuca, P., & Vitanza, A. (2026). The Interplay Between Functionality and Complexity in Neural Network Efficiency. In AIxIA 2026 – Advances in Artificial Intelligence, pp. 514-527. DOI: https://doi.org/10.1007/978-3-032-40774-0_37

• Conference paper: Pagliuca, P., & Vitanza, A. (2026). What Evolutionary Algorithm(s) Should We Use? Towards Preliminary Guidelines from an Exploratory Analysis of Foraging Problems. In Proceedings of the 2026 22nd International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT 2026), pp. 846-851. DOI: https://doi.org/10.1109/DCOSS-IoT69657.2026.00130

2025

• 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

• 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., De Marco, S., Vitanza, A., Milano, N., Rega, A. & Nolfi, S. (2025). A Preliminary Study on Assessing Patient Behavioral Quality Using Deep Learning. In 2025 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) (pp. 1230-1235), IEEE. DOI: https://doi.org/10.1109/MetroXRAINE66377.2025.11340568

• 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

• 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., 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., Tufo, G., Zribi, M., & Pitolli, F. (2025). Detection of Drug Abuse through CNNs and ViTs: a Comparative Analysis of Performance and Explainability. In Image Analysis and Processing - ICIAP 2025 Workshops. DOI: https://doi.org/10.1007/978-3-032-11381-8_2

• 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

• Conference paper: Pagliuca, P., & Vitanza, A. (2025).  Integrating a Quantitative Approach to Deep Learning for Patient Monitoring. In Proceedings of the 14th Italian Forum of Ambient Assisted Living (ForItAAL 2025) (pp. 167-183). DOI: https://doi.org/10.1007/978-3-032-11050-3_11

• 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

• 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

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: 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: 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.

• 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: 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.

• 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

• 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: 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: 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

2023

• 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.

• 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

• 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

2022

• 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

• 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

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: 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

• 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

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