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Advances in Learning Automata and Intelligent Optimization (Intelligent Systems Reference Library,208) 1st ed. 2021 Edition
by 作者: Javidan Kazemi Kordestani，Mehdi Razapoor Mirsaleh，Alireza Rezvanian，Mohammad Reza Meybodi
Publisher Finelybook 出版社: ; 1st ed. 2021 edition (June 24,2021)
Language 语言: English
pages 页数: 360 pages
ISBN-10 书号: 3030762904
ISBN-13 书号: 9783030762902
Book Description
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.
