Springer Cellular Learning Automata: Theory and Applications: 307 (Studies in Systems, Decision and Control, 307)

This book highlights both theoretical and applied advances in cellular learning automata (CLA), a type of hybrid computational model that has been successfully employed in various areas to solve complex problems and to model, learn, or simulate complicated patterns of behavior. Owing to CLA’s parallel and learning abilities, it has proven to be quite effective in uncertain, time-varying, decentralized, and distributed environments.  The book begins with a brief introduction to various CLA models, before focusing on recently developed CLA variants. In turn, the research areas related to CLA are addressed as bibliometric network analysis perspectives. The next part of the book presents CLA-based solutions to several computer science problems in e.g. static optimization, dynamic optimization, wireless networks, mesh networks, and cloud computing. Given its scope, the book is well suited for all researchers in the fields of artificial intelligence and reinforcement learning.   From the Back Cover This book highlights both theoretical and applied advances in cellular learning automata (CLA), a type of hybrid computational model that has been successfully employed in various areas to solve complex problems and to model, learn, or simulate complicated patterns of behavior. Owing to CLA’s parallel and learning abilities, it has proven to be quite effective in uncertain, time-varying, decentralized, and distributed environments.  The book begins with a brief introduction to various CLA models, before focusing on recently developed CLA variants. In turn, the research areas related to CLA are addressed as bibliometric network analysis perspectives. The next part of the book presents CLA-based solutions to several computer science problems in e.g. static optimization, dynamic optimization, wireless networks, mesh networks, and cloud computing. Given its scope, the book is well suited for all researchers in the fields of artificial intelligence and reinforcement learning.   About the Author Reza Vafashoar received the B.S. degree in Computer Engineering from Urmia University, Urmia, Iran, in 2007, and the M.S. degree in Artificial Intelligence from Amirkabir University of Technology, Tehran,Iran, in 2010. He also received the Ph.D. degree in Computer Engineering at the Computer Engineering Department from Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran, in 2019. His research interests include learning systems, cellular learning automata, evolutionary computing, and other computational intelligence techniques. Hossein Morshedlou received the B.Sc. degree in computer engineering from Ferdowsi University,Mashhad, Iran, and the M.Sc. degree in computer engineering from the Amirkabir University of Technology, Tehran, Iran, in 2005 and 2008,respectively. He also received the Ph.D. degree in Computer Engineering at the Computer Engineering Department from Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran, in 2017. Since 2019, he has been an assistant professor with the Faculty of Computer Engineering and Information Technology, Shahrood University of Technology, Shahrood, Iran. His research interests include distributed systems, cloud computing, learning automata, reinforcement learning, parallel algorithms, and soft computing. Alireza Rezvanian received the B.Sc. degree from Bu-Ali Sina University of Hamedan, Iran, in 2007, the M.Sc. degree in Computer Engineering with honors from Islamic Azad University of Qazvin, Iran, in 2010, and the Ph.D. degree in Computer Engineering at the Computer Engineering Department from Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran, in 2016. Currently, he is an Assistant Professor with the Department of Computer Engineering, University of Science and Culture, Tehran, Iran. He worked from 2016 to 2020 as a researcher at the School of Computer Science from the Institut