Go to main content

Advances in learning automata and intelligent optimization / Javidan Kazemi Kordestani, Mehdi Razapoor Mirsaleh, Alireza Rezvanian, Mohammad Reza Meybodi, editors.

Kazemi Kordestani, Javidan, editor.; Mirsaleh, Mehdi Razapoor, editor.; Rezvanian, Alireza, editor.; Meybodi, Mohammad Reza, 1952- editor.
2021
Q325.5 .A38 2021
Available Online
Formats
Format
BibTeX
MARCXML
TextMARC
MARC
DublinCore
EndNote
NLM
RefWorks
RIS
Add to Basket
Cite
Citation

Linked e-resources

Linked Resource
Online Access
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks

Details

Title
Advances in learning automata and intelligent optimization / Javidan Kazemi Kordestani, Mehdi Razapoor Mirsaleh, Alireza Rezvanian, Mohammad Reza Meybodi, editors.
ISBN
9783030762919 (electronic bk.)
3030762912 (electronic bk.)
9783030762902
3030762904
DOI
https://doi.org/10.1007/978-3-030-76291-9
Published
Cham : Springer, [2021]
Copyright
©2021
Language
English
Description
1 online resource : illustrations (chiefly color).
Item Number
10.1007/978-3-030-76291-9 doi
Call Number
Q325.5 .A38 2021
Dewey Decimal Classification
006.3/1
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, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed July 6, 2021).
Added Author
Kazemi Kordestani, Javidan, editor.
Mirsaleh, Mehdi Razapoor, editor.
Rezvanian, Alireza, editor.
Meybodi, Mohammad Reza, 1952- editor.
Series
Intelligent systems reference library ; v. 208.
Available in Other Form
Advances in learning automata and intelligent optimization.
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
All Resources
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.

Browse Subjects

Show more subjects...

Statistics

from
to
Export