25 citations · 41 across the 11 of their papers we have counts for
12 papers
On the Unexpected Abilities of Large Language Models
Stefano Nolfi
Large Language Models (LLMs) are capable of displaying a wide range of abilities that are not directly connected with the task for which they are trained: predicting the next words…
The Role of Morphological Variation in Evolutionary Robotics: Maximizing Performance and Robustness
Jonata Tyska Carvalho, Stefano Nolfi
Exposing an Evolutionary Algorithm that is used to evolve robot controllers to variable conditions is necessary to obtain solutions which are robust and can cross the reality gap.…
Qualitative Differences Between Evolutionary Strategies and Reinforcement Learning Methods for Control of Autonomous Agents
Nicola Milano, Stefano Nolfi
In this paper we analyze the qualitative differences between evolutionary strategies and reinforcement learning algorithms by focusing on two popular state-of-the-art algorithms: t…
Automated Curriculum Learning for Embodied Agents: A Neuroevolutionary Approach
Nicola Milano, Stefano Nolfi
We demonstrate how an evolutionary algorithm can be extended with a curriculum learning process that selects automatically the environmental conditions in which the evolving agents…
The Dynamic of Body and Brain Co-Evolution
Paolo Pagliuca, Stefano Nolfi
We introduce a method that permits to co-evolve the body and the control properties of robots. It can be used to adapt the morphological traits of robots with a hand-designed morph…
Autonomous Learning of Features for Control: Experiments with Embodied and Situated Agents
Nicola Milano, Stefano Nolfi
As discussed in previous studies, the efficacy of evolutionary or reinforcement learning algorithms for continuous control optimization can be enhanced by including a neural module…
Efficacy of Modern Neuro-Evolutionary Strategies for Continuous Control Optimization
Paolo Pagliuca, Nicola Milano, Stefano Nolfi
We analyze the efficacy of modern neuro-evolutionary strategies for continuous control optimization. Overall, the results collected on a wide variety of qualitatively different ben…
Long-Term Progress and Behavior Complexification in Competitive Co-Evolution
Luca Simione, Stefano Nolfi
The possibility to use competitive evolutionary algorithms to generate long-term progress is normally prevented by the convergence on limit cycle dynamics in which the evolving age…
Scaling Up Cartesian Genetic Programming through Preferential Selection of Larger Solutions
Nicola Milano, Stefano Nolfi
We demonstrate how efficiency of Cartesian Genetic Programming method can be scaled up through the preferential selection of phenotypically larger solutions, i.e. through the prefe…
Robust Optimization through Neuroevolution
Paolo Pagliuca, Stefano Nolfi
We propose a method for evolving solutions that are robust with respect to variations of the environmental conditions (i.e. that can operate effectively in new conditions immediate…
Robustness, Evolvability and Phenotypic Complexity: Insights from Evolving Digital Circuits
Nicola Milano, Paolo Pagliuca, Stefano Nolfi
We show how the characteristics of the evolutionary algorithm influence the evolvability of candidate solutions, i.e. the propensity of evolving individuals to generate better solu…
Moderate Environmental Variation Promotes the Evolution of Robust Solutions
Nicola Milano, Jônata Tyska Carvalho, Stefano Nolfi
Previous evolutionary studies demonstrated how evaluating evolving agents in variable environmental conditions enable them to develop solutions that are robust to environmental var…