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ORCIDNicola Milano
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2020 – today
- 2024
[j6]Nicola Milano
, Stefano Nolfi:
Qualitative differences between evolutionary strategies and reinforcement learning methods for control of autonomous agents. Evol. Intell. 17(2): 1185-1195 (2024)- [c8]Monica Casella, Maria Luongo, Davide Marocco, Nicola Milano, Michela Ponticorvo:
LLM embeddings on test items predict post hoc loadings in personality tests. Ital-IA 2024: 313-317 - [c7]Maria Luongo, Michela Ponticorvo, Nicola Milano:
Exploring Spatial Cognition: Comparative Analysis of Agent-Based Models in Dynamic and Static Environments. IWINAC 2024: 253-260 - 2023
- [c6]Monica Casella, Pasquale Dolce, Raffaella Esposito, Maria Luongo, Davide Marocco, Nicola Milano, Michela Ponticorvo, Roberta Simeoli:
Exploring Psychological Data by Integrating Explanatory and Predictive Approaches through Artificial Neural Networks: A Brief Overview of Current Applications. Ital-IA 2023: 166-170 - [c5]Raffaella Esposito, Monica Casella, Nicola Milano, Davide Marocco:
Autoencoders as a Tool to Detect Nonlinear Relationships in Latent Variables Models. MetroXRAINE 2023: 1012-1016 - 2022
- [j5]Nicola Milano, Stefano Nolfi:
Phenotypic complexity and evolvability in evolving robots. Frontiers Robotics AI 9 (2022) - [c4]Nicola Milano, Michela Ponticorvo:
Spatial Frames of Reference and Action: A Study with Evolved Neuro-agents. IWINAC (1) 2022: 515-523 - [c3]Paolo Pagliuca, Nicola Milano, Stefano Nolfi:
Automated Categorization of Behavioral Quality Through Deep Neural Networks. MetroXRAINE 2022: 372-376 - [i7]Nicola Milano, Stefano Nolfi:
Qualitative Differences Between Evolutionary Strategies and Reinforcement Learning Methods for Control of Autonomous Agents. CoRR abs/2205.07592 (2022) - 2021
- [b1]Nicola Milano:
On the relation between robustness, evolvability and phenotypic complexity: insight from artificial evolutionary experiments. University of Plymouth, UK, 2021 - [j4]Nicola Milano, Stefano Nolfi:
Enhancing Cartesian genetic programming through preferential selection of larger solutions. Evol. Intell. 14(4): 1539-1546 (2021) - [i6]Nicola Milano, Stefano Nolfi:
Automated Curriculum Learning for Embodied Agents: A Neuroevolutionary Approach. CoRR abs/2102.08849 (2021) - 2020
- [j3]Paolo Pagliuca, Nicola Milano, Stefano Nolfi:
Efficacy of Modern Neuro-Evolutionary Strategies for Continuous Control Optimization. Frontiers Robotics AI 7: 98 (2020) - [i5]Nicola Milano, Stefano Nolfi:
Autonomous Learning of Features for Control: Experiments with Embodied and Situated Agents. CoRR abs/2009.07132 (2020)
2010 – 2019
- 2019
- [j2]Nicola Milano, Paolo Pagliuca, Stefano Nolfi:
Robustness, evolvability and phenotypic complexity: insights from evolving digital circuits. Evol. Intell. 12(1): 83-95 (2019) - [i4]Paolo Pagliuca, Nicola Milano, Stefano Nolfi:
Efficacy of Modern Neuro-Evolutionary Strategies for Continuous Control Optimization. CoRR abs/1912.05239 (2019) - 2018
- [j1]Nicola Milano, Jônata Tyska Carvalho, Stefano Nolfi:
Moderate Environmental Variation Across Generations Promotes the Evolution of Robust Solutions. Artif. Life 24(4) (2018) - [c2]Jônata Tyska Carvalho, Nicola Milano, Stefano Nolfi:
Evolving Robust Solutions for Stochastically Varying Problems. CEC 2018: 1-8 - [i3]Nicola Milano, Stefano Nolfi:
Scaling Up Cartesian Genetic Programming through Preferential Selection of Larger Solutions. CoRR abs/1810.09485 (2018) - 2017
- [c1]Nicola Milano, Jônata Tyska Carvalho, Stefano Nolfi:
Environmental variations promotes adaptation in artificial evolution. SSCI 2017: 1-7 - [i2]Nicola Milano, Jônata Tyska Carvalho, Stefano Nolfi:
Moderate Environmental Variation Promotes Adaptation in Artificial Evolution. CoRR abs/1710.07913 (2017) - [i1]Nicola Milano, Paolo Pagliuca, Stefano Nolfi:
Robustness, Evolvability and Phenotypic Complexity: Insights from Evolving Digital Circuits. CoRR abs/1712.04254 (2017)
Coauthor Index
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