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Title Advances in learning automata and intelligent optimization / Javidan Kazemi Kordestani, Mehdi Razapoor Mirsaleh, Alireza Rezvanian, Mohammad Reza Meybodi, editors
Published Cham : Springer, [2021]
©2021

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Description 1 online resource : illustrations (chiefly color)
Series Intelligent systems reference library, 1868-4394 ; volume 208
Intelligent systems reference library ; v. 208. 1868-4394
Contents 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
Summary 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
Bibliography Includes bibliographical references
Notes Online resource; title from PDF title page (SpringerLink, viewed July 6, 2021)
Subject Machine learning.
Mathematical optimization.
Machine learning
Mathematical optimization
Aprenentatge automàtic.
Optimització matemàtica.
maquines.
Genre/Form Llibres electrònics.
Form Electronic book
Author Kazemi Kordestani, Javidan, editor
Mirsaleh, Mehdi Razapoor, editor
Rezvanian, Alireza, editor
Meybodi, Mohammad Reza, 1952- editor.
ISBN 9783030762919
3030762912