5 papers
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…
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…
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…
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…