25 citations · 26 across the 5 of their papers we have counts for
4 papers · 1 filter
Learning and evolution: factors influencing an effective combination
Paolo Pagliuca
The mutual relationship between evolution and learning is a controversial argument among the artificial intelligence and neuro-evolution communities. After more than three decades,…
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…
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…