Advances in Learning Automata and Intelligent Optimization / Intelligent Systems Reference Library Bd.208 (PDF)
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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 arediscussed.
. 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.
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
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.
- 2021, 1st ed. 2021, 340 Seiten, Englisch
- Herausgegeben: Javidan Kazemi Kordestani, Mehdi Razapoor Mirsaleh, Alireza Rezvanian, Mohammad Reza Meybodi
- Verlag: Springer International Publishing
- ISBN-10: 3030762912
- ISBN-13: 9783030762919
- Erscheinungsdatum: 23.06.2021
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