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Found 12 papers, 0 papers with code
On the Unexpected Abilities of Large Language Models
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 of human-written texts.
The Role of Morphological Variation in Evolutionary Robotics: Maximizing Performance and Robustness
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
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: the OpenAI-ES evolutionary strategy and the Proximal Policy Optimization (PPO) reinforcement learning algorithm -- the most similar methods of the two families.
Automated Curriculum Learning for Embodied Agents: A Neuroevolutionary Approach
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 are evaluated.
The Dynamic of Body and Brain Co-Evolution
We introduce a method that permits to co-evolve the body and the control properties of robots.
Autonomous Learning of Features for Control: Experiments with Embodied and Situated Agents
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 dedicated to feature extraction trained through self-supervised methods.
Efficacy of Modern Neuro-Evolutionary Strategies for Continuous Control Optimization
We analyze the efficacy of modern neuro-evolutionary strategies for continuous control optimization.
Long-Term Progress and Behavior Complexification in Competitive Co-Evolution
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 agents keep progressing against their current competitors by periodically rediscovering solutions adopted previously over and over again.
Scaling Up Cartesian Genetic Programming through Preferential Selection of Larger Solutions
Finally, for problems like the Paige regression in which neutrality plays a minor role, the advantage of the preferential selection of larger solutions can be extended by preferring larger solutions also among quasi-neutral alternative candidate solutions, i. e. solutions achieving slightly different performance.
Robust Optimization through Neuroevolution
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 immediately, without the need to adapt to variations).
Robustness, Evolvability and Phenotypic Complexity: Insights from Evolving Digital Circuits
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 solutions as a result of genetic variation.
Moderate Environmental Variation Promotes the Evolution of Robust Solutions
Previous evolutionary studies demonstrated how evaluating evolving agents in variable environmental conditions enable them to develop solutions that are robust to environmental variation.
