Environmental variations promotes adaptation in artificial evolution. - Nicola Milano; Jônata Tyska Carvalho; Stefano Nolfi (SSCI, 2017)

Moderate Environmental Variation Promotes Adaptation in Artificial Evolution. - Nicola Milano; Jônata Tyska Carvalho; Stefano Nolfi (CoRR, 2017)

Evolving Robust Solutions for Stochastically Varying Problems. - Jônata Tyska Carvalho; Nicola Milano; Stefano Nolfi (CEC, 2018)

Moderate Environmental Variation Across Generations Promotes the Evolution of Robust Solutions. - Nicola Milano; Jônata Tyska Carvalho; Stefano Nolfi (Artif. Life, 2018)

Scaling Up Cartesian Genetic Programming through Preferential Selection of Larger Solutions. - Nicola Milano; Stefano Nolfi (CoRR, 2018)

Robustness, evolvability and phenotypic complexity: insights from evolving digital circuits. - Nicola Milano; Paolo Pagliuca; Stefano Nolfi (Evol. Intell., 2019)

Autonomous Learning of Features for Control: Experiments with Embodied and Situated Agents. - Nicola Milano; Stefano Nolfi (CoRR, 2020)

Efficacy of Modern Neuro-Evolutionary Strategies for Continuous Control Optimization. - Paolo Pagliuca; Nicola Milano; Stefano Nolfi (Frontiers Robotics AI, 2020)

Automated Curriculum Learning for Embodied Agents: A Neuroevolutionary Approach. - Nicola Milano; Stefano Nolfi (CoRR, 2021)

Enhancing Cartesian genetic programming through preferential selection of larger solutions. - Nicola Milano; Stefano Nolfi (Evol. Intell., 2021)

On the relation between robustness, evolvability and phenotypic complexity: insight from artificial evolutionary experiments. - Nicola Milano (2021)

Spatial Frames of Reference and Action: A Study with Evolved Neuro-agents. - Nicola Milano; Michela Ponticorvo (IWINAC (1), 2022)

Automated Categorization of Behavioral Quality Through Deep Neural Networks. - Paolo Pagliuca; Nicola Milano; Stefano Nolfi (MetroXRAINE, 2022)

Phenotypic complexity and evolvability in evolving robots. - Nicola Milano; Stefano Nolfi (Frontiers Robotics AI, 2022)

Exploring Psychological Data by Integrating Explanatory and Predictive Approaches through Artificial Neural Networks: A Brief Overview of Current Applications. - Monica Casella; Pasquale Dolce; Raffaella Esposito; Maria Luongo; Davide Marocco; Nicola Milano; Michela Ponticorvo; Roberta Simeoli (Ital-IA, 2023)

Autoencoders as a Tool to Detect Nonlinear Relationships in Latent Variables Models. - Raffaella Esposito; Monica Casella; Nicola Milano; Davide Marocco (MetroXRAINE, 2023)

Not Only ChatGPT: Educational Approaches for Embodied Artificial Intelligence (full paper). - Monica Casella; Clara Nobile; Raffaella Esposito; Maria Luongo; Alessio Manfredini; Nicola Milano; Onofrio Gigliotta; Davide Marocco; Michela Ponticorvo (edu4AI@AI*IA, 2024)

LLM embeddings on test items predict post hoc loadings in personality tests. - Monica Casella; Maria Luongo; Davide Marocco; Nicola Milano; Michela Ponticorvo (Ital-IA, 2024)

Exploring Spatial Cognition: Comparative Analysis of Agent-Based Models in Dynamic and Static Environments. - Maria Luongo; Michela Ponticorvo; Nicola Milano (IWINAC, 2024)

Modeling Nonlinear Relationships in Psychometric Data Using Variational Autoencoders: Insights from Simulated Data. - Monica Casella; Nicola Milano; Raffaella Esposito; Davide Marocco (MetroXRAINE, 2024)

Interaction Rules Supporting Effective Flocking Behavior. - Nicola Milano; Stefano Nolfi (Artif. Life, 2024)

Qualitative differences between evolutionary strategies and reinforcement learning methods for control of autonomous agents. - Nicola Milano; Stefano Nolfi (Evol. Intell., 2024)

Minimizing learning experiences in embodied agents language and action learning. - Nicola Milano; Stefano Nolfi (Neurocomputing, 2025)

Comparing Human Expertise and Large Language Models Embeddings in Content Validity Assessment of Personality Tests. - Nicola Milano; Michela Ponticorvo; Davide Marocco (CoRR, 2025)