Advances in Learning Automata and Intelligent Optimization

This book is devoted to the leading research in applying learning automaton (LA) and heuristics for solving benchmark and real-world optimization problems. The ever-increasing application of the LA as a promising reinforcement learning technique in artificial intelligence makes it necessary to provide scholars, scientists, and engineers with a practical discussion on LA solutions for optimization. The book starts with a brief introduction to LA models for optimization. Afterward, the research areas related to LA and optimization are addressed as bibliometric network analysis. Then, LA's application in behavior control in evolutionary computation, and memetic models of object migration automata and cellular learning automata for solving NP hard problems are considered. Next, an overview of multi-population methods for DOPs, LA's application in dynamic optimization problems (DOPs), and the function evaluation management in evolutionary multi-population for DOPs are discussed.

Highlighted benefits

• Presents the latest advances in learning automata-based optimization approaches.
• Addresses the memetic models of learning automata for solving NP-hard problems.
• Discusses the application of learning automata for behavior control in evolutionary computation in detail.
• Gives the fundamental principles and analyses of the different concepts associated with multi-population methods for dynamic optimization problems.

Provides leading studies in the application of learning automata (LA) along with different heuristics for solving optimization problems Collects recent advances and developments in learning automata-based heuristics for optimization in both algorithms and application perspectives Addresses key topics and issues regarding the models, theories, algorithms, and applications of learning automata for intelligent optimization

Autorentext

Javidan Kazemi Kordestani received the B.Sc. in Computer Engineering (Software Engineering) from the Islamic Azad University of Karaj, Iran in 2008, and his M.Sc. in Computer Engineering (Artificial Intelligence) from Islamic Azad University of Qazvin, Iran in 2012. He also received the Ph.D. degree in Computer Engineering (Artificial Intelligence) at the Department of Electrical, Computer and IT Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. He has authored or co-authored numerous research publications in reputable peer-reviewed journals of Elsevier, Springer, and Taylor & Francis. He has also acted as a reviewer for several prestigious international journals. His current research interests include evolutionary computation, dynamic optimization problems, learning systems, and real-world applications.

Mehdi Rezapoor Mirsaleh received the B.Sc. in Computer Engineering from Kharazmi University, Tehran, Iran, in 2000. He also received the M.Sc. and Ph.D. degrees from Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran, in 2003 and 2016, respectively, in Computer Engineering. Currently, he is an Assistant Professor in the Department of Computer Engineering and Information Technology, Payame Noor University (PNU), Tehran, Iran. His research interests include learning systems, machine learning, social networks, 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 Institute for Research in Fundamental Sciences (IPM), Tehran, Iran. He has authored or co-authored more than 70 research publications in reputable peer-reviewed journals and conferences, including IEEE, Elsevier, Springer, Wiley, and Taylor & Francis. He has been a guest editor of the special issue on new applications of learning automata-based techniques in real-world environments for the journal of computational science (Elsevier). He is an editorial board member and one of the associate editors of human-centric computing and information sciences (Springer), CAAI Transactions on Intelligence Technology (IET), The Journal of Engineering (IET), and Data in Brief (Elsevier). His research activities include soft computing, learning automata, complex networks, social network analysis, data mining, data science, machine learning, and evolutionary algorithms.

Mohammad Reza Meybodi received the B.S. and M.S. degrees in Economics from the Shahid Beheshti University in Iran in 1973 and 1977, respectively. He also received the M.S. and Ph.D. degrees from Oklahoma University, USA, in 1980 and 1983, respectively, in Computer Science. Currently, he is a Full Professor in the Computer Engineering Department, Amirkabir University of Technology, Tehran, Iran. Prior to the current position, he worked from 1983 to 1985 as an Assistant Professor at the Western Michigan University and from 1985 to 1991 as an Associate Professor at Ohio University, USA. His current research interests include learning systems, cloud computing, soft computing, and social networks.

Klappentext

An Introduction to learning automata and optimization.- Learning automaton and its variants for optimization: a bibliometric analysis.- Cellular automata, learning automata, and cellular learning automata for optimization.- Learning automata for behavior control in evolutionary computation.- A memetic model based on fixed structure learning automata for solving NP-Hard problems.

Inhalt
An Introduction to learning automata and optimization.- Learning automaton and its variants for optimization: a bibliometric analysis.- Cellular automata, learning automata, and cellular learning automata for optimization.- Learning automata for behavior control in evolutionary computation.- A memetic model based on fixed structure learning automata for solving NP-Hard problems.