Evolutionary robotics applies the selection, variation, and heredity principles of natural
evolution to the design of robots with embodied intelligence. It can be considered as a …

Artificial neural networks (ANNs) are applied to many real-world problems, ranging from
pattern classification to robot control. In order to design a neural network for a particular task …

A comprehensive introduction to new approaches in artificial intelligence and robotics that
are inspired by self-organizing biological processes and structures. New approaches to …

Lifelong learning and adaptability are two defining aspects of biological agents. Modern
reinforcement learning (RL) approaches have shown significant progress in solving complex …

Biological neural networks are systems of extraordinary computational capabilities shaped
by evolution, development, and lifelong learning. The interplay of these elements leads to …

This paper surveys research on applying neuroevolution (NE) to games. In neuroevolution,
artificial neural networks are trained through evolutionary algorithms, taking inspiration from …

This paper investigates how an evolutionary algorithm with an indirect encoding exploits the
property of phenotypic regularity, an important design principle found in natural organisms …

Abstract memory in biological neural networks. Similarly, artificial neural networks could
benefit from modulatory dynamics when facing certain types of learning problem. Here we …

A long-term goal of AI is to produce agents that can learn a diversity of skills throughout their
lifetimes and continuously improve those skills via experience. A longstanding obstacle …

Biological brains can adapt and learn from past experience. Yet neuroevolution, that is,
automatically creating artificial neural networks (ANNs) through evolutionary algorithms, has …