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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 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.
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