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This book highlights both theoretical and applied advances in
cellular learning automata (CLA), a type of hybrid computational
model that has been successfully employed in various areas to solve
complex problems and to model, learn, or simulate complicated
patterns of behavior. Owing to CLA's parallel and learning
abilities, it has proven to be quite effective in uncertain,
time-varying, decentralized, and distributed environments. The book
begins with a brief introduction to various CLA models, before
focusing on recently developed CLA variants. In turn, the research
areas related to CLA are addressed as bibliometric network analysis
perspectives. The next part of the book presents CLA-based
solutions to several computer science problems in e.g. static
optimization, dynamic optimization, wireless networks, mesh
networks, and cloud computing. Given its scope, the book is well
suited for all researchers in the fields of artificial intelligence
and reinforcement learning.
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.
This book highlights both theoretical and applied advances in
cellular learning automata (CLA), a type of hybrid computational
model that has been successfully employed in various areas to solve
complex problems and to model, learn, or simulate complicated
patterns of behavior. Owing to CLA's parallel and learning
abilities, it has proven to be quite effective in uncertain,
time-varying, decentralized, and distributed environments. The book
begins with a brief introduction to various CLA models, before
focusing on recently developed CLA variants. In turn, the research
areas related to CLA are addressed as bibliometric network analysis
perspectives. The next part of the book presents CLA-based
solutions to several computer science problems in e.g. static
optimization, dynamic optimization, wireless networks, mesh
networks, and cloud computing. Given its scope, the book is well
suited for all researchers in the fields of artificial intelligence
and reinforcement learning.
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.
This book begins by briefly explaining learning automata (LA)
models and a recently developed cellular learning automaton (CLA)
named wavefront CLA. Analyzing social networks is increasingly
important, so as to identify behavioral patterns in interactions
among individuals and in the networks' evolution, and to develop
the algorithms required for meaningful analysis. As an emerging
artificial intelligence research area, learning automata (LA) has
already had a significant impact in many areas of social networks.
Here, the research areas related to learning and social networks
are addressed from bibliometric and network analysis perspectives.
In turn, the second part of the book highlights a range of LA-based
applications addressing social network problems, from network
sampling, community detection, link prediction, and trust
management, to recommender systems and finally influence
maximization. Given its scope, the book offers a valuable guide for
all researchers whose work involves reinforcement learning, social
networks and/or artificial intelligence.
This book collects recent theoretical advances and concrete
applications of learning automata (LAs) in various areas of
computer science, presenting a broad treatment of the computer
science field in a survey style. Learning automata (LAs) have
proven to be effective decision-making agents, especially within
unknown stochastic environments. The book starts with a brief
explanation of LAs and their baseline variations. It subsequently
introduces readers to a number of recently developed, complex
structures used to supplement LAs, and describes their steady-state
behaviors. These complex structures have been developed because, by
design, LAs are simple units used to perform simple tasks; their
full potential can only be tapped when several interconnected LAs
cooperate to produce a group synergy. In turn, the next part of the
book highlights a range of LA-based applications in diverse
computer science domains, from wireless sensor networks, to
peer-to-peer networks, to complex social networks, and finally to
Petri nets. The book accompanies the reader on a comprehensive
journey, starting from basic concepts, continuing to recent
theoretical findings, and ending in the applications of LAs in
problems from numerous research domains. As such, the book offers a
valuable resource for all computer engineers, scientists, and
students, especially those whose work involves the reinforcement
learning and artificial intelligence domains.
This book examines the intelligent random walk algorithms based on
learning automata: these versions of random walk algorithms
gradually obtain required information from the nature of the
application to improve their efficiency. The book also describes
the corresponding applications of this type of random walk
algorithm, particularly as an efficient prediction model for
large-scale networks such as peer-to-peer and social networks. The
book opens new horizons for designing prediction models and
problem-solving methods based on intelligent random walk
algorithms, which are used for modeling and simulation in various
types of networks, including computer, social and biological
networks, and which may be employed a wide range of real-world
applications.
This book collects recent theoretical advances and concrete
applications of learning automata (LAs) in various areas of
computer science, presenting a broad treatment of the computer
science field in a survey style. Learning automata (LAs) have
proven to be effective decision-making agents, especially within
unknown stochastic environments. The book starts with a brief
explanation of LAs and their baseline variations. It subsequently
introduces readers to a number of recently developed, complex
structures used to supplement LAs, and describes their steady-state
behaviors. These complex structures have been developed because, by
design, LAs are simple units used to perform simple tasks; their
full potential can only be tapped when several interconnected LAs
cooperate to produce a group synergy. In turn, the next part of the
book highlights a range of LA-based applications in diverse
computer science domains, from wireless sensor networks, to
peer-to-peer networks, to complex social networks, and finally to
Petri nets. The book accompanies the reader on a comprehensive
journey, starting from basic concepts, continuing to recent
theoretical findings, and ending in the applications of LAs in
problems from numerous research domains. As such, the book offers a
valuable resource for all computer engineers, scientists, and
students, especially those whose work involves the reinforcement
learning and artificial intelligence domains.
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