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portada Advances in Learning Automata and Intelligent Optimization: 208 (Intelligent Systems Reference Library)
Type
Physical Book
Illustrated by
Publisher
Author
Year
2021
Language
English
Pages
340
Format
Hardcover
Dimensions
23.4x15.6x2.1 cm
Weight
0.68 kg.
ISBN13
9783030762902
Edition No.
1
Categories

Advances in Learning Automata and Intelligent Optimization: 208 (Intelligent Systems Reference Library)

Javidan Kazemi Kordestani (Illustrated by) · Mehdi Razapoor Mirsaleh (Illustrated by) · Alireza Rezvanian (Illustrated by) · Springer · Hardcover

Advances in Learning Automata and Intelligent Optimization: 208 (Intelligent Systems Reference Library) - Kazemi Kordestani, Javidan ; Mirsaleh, Mehdi Razapoor ; Rezvanian, Alireza

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Synopsis "Advances in Learning Automata and Intelligent Optimization: 208 (Intelligent Systems Reference Library) "

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.

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