Bio‐Inspired Learning and Adaptation for Optimization and Control of Complex Systems
Jing Na, Zhile Yang, Shyam Kamal, Liang Hu, Wenbo Wang, Yimin Zhou · Complexity · 2019
Learning and adaptation play an important role in solving numerous science and engineering problems, including artificial intelligence, control engineering, and many multidisciplinary topics. In this respect, a series of bioinspired methods, such as reinforcement learning, coevolution learning, and approximate dynamic programming as well as swarm evolutions, provide essential theoretical tools for solving various optimization and control problems. This has stimulated great research interests and developments on learning and adaptation. This special issue aims at providing a specific opportunity to review the state-of-the-art of this recently emerging and cross-disciplinary field of bio-inspired learning and adaptation. In this special issue, we bring together researchers to present the latest progress, novel research methodologies, and a broad spectrum of potential research topics.