Nicola Milano

Orcid: 0000-0002-1604-5161

According to our database1, Nicola Milano authored at least 17 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

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Bibliography

2024
Qualitative differences between evolutionary strategies and reinforcement learning methods for control of autonomous agents.
Evol. Intell., April, 2024

2023
Autoencoders as a Tool to Detect Nonlinear Relationships in Latent Variables Models.
Proceedings of the IEEE International Conference on Metrology for eXtended Reality, 2023

Exploring Psychological Data by Integrating Explanatory and Predictive Approaches through Artificial Neural Networks: A Brief Overview of Current Applications.
Proceedings of the Italia Intelligenza Artificiale, 2023

2022
Phenotypic complexity and evolvability in evolving robots.
Frontiers Robotics AI, 2022

Automated Categorization of Behavioral Quality Through Deep Neural Networks.
Proceedings of the IEEE International Conference on Metrology for Extended Reality, 2022

Spatial Frames of Reference and Action: A Study with Evolved Neuro-agents.
Proceedings of the Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence, 2022

2021
On the relation between robustness, evolvability and phenotypic complexity: insight from artificial evolutionary experiments.
PhD thesis, 2021

Enhancing Cartesian genetic programming through preferential selection of larger solutions.
Evol. Intell., 2021

Automated Curriculum Learning for Embodied Agents: A Neuroevolutionary Approach.
CoRR, 2021

2020
Efficacy of Modern Neuro-Evolutionary Strategies for Continuous Control Optimization.
Frontiers Robotics AI, 2020

Autonomous Learning of Features for Control: Experiments with Embodied and Situated Agents.
CoRR, 2020

2019
Robustness, evolvability and phenotypic complexity: insights from evolving digital circuits.
Evol. Intell., 2019

2018
Scaling Up Cartesian Genetic Programming through Preferential Selection of Larger Solutions.
CoRR, 2018

Moderate Environmental Variation Across Generations Promotes the Evolution of Robust Solutions.
Artif. Life, 2018

Evolving Robust Solutions for Stochastically Varying Problems.
Proceedings of the 2018 IEEE Congress on Evolutionary Computation, 2018

2017
Moderate Environmental Variation Promotes Adaptation in Artificial Evolution.
CoRR, 2017

Environmental variations promotes adaptation in artificial evolution.
Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017


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