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Books > Computing & IT > Applications of computing > Artificial intelligence
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Principles in Noisy Optimization - Applied to Multi-agent Coordination (Hardcover, 1st ed. 2018)
Loot Price: R2,705
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Principles in Noisy Optimization - Applied to Multi-agent Coordination (Hardcover, 1st ed. 2018)
Series: Cognitive Intelligence and Robotics
Expected to ship within 18 - 22 working days
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Noisy optimization is a topic of growing interest for researchers
working on mainstream optimization problems. Although several
techniques for dealing with stochastic noise in optimization
problems are covered in journals and conference proceedings, today
there are virtually no books that approach noisy optimization from
a layman's perspective; this book remedies that gap. Beginning with
the foundations of evolutionary optimization, the book subsequently
explores the principles of noisy optimization in single and
multi-objective settings, and presents detailed illustrations of
the principles developed for application in real-world multi-agent
coordination problems. Special emphasis is given to the design of
intelligent algorithms for noisy optimization in real-time
applications. The book is unique in terms of its content, writing
style and above all its simplicity, which will appeal to readers
with a broad range of backgrounds. The book is divided into 7
chapters, the first of which provides an introduction to Swarm and
Evolutionary Optimization algorithms. Chapter 2 includes a thorough
review of agent architectures for multi-agent coordination. In
turn, Chapter 3 provides an extensive review of noisy optimization,
while Chapter 4 addresses issues of noise handling in the context
of single-objective optimization problems. An illustrative case
study on multi-robot path-planning in the presence of measurement
noise is also highlighted in this chapter. Chapter 5 deals with
noisy multi-objective optimization and includes a case study on
noisy multi-robot box-pushing. In Chapter 6, the authors examine
the scope of various algorithms in noisy optimization problems.
Lastly, Chapter 7 summarizes the main results obtained in the
previous chapters and elaborates on the book's potential with
regard to real-world noisy optimization problems.
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