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Stochastic Adaptive Search for Global Optimization (Paperback, Softcover reprint of the original 1st ed. 2003)
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Stochastic Adaptive Search for Global Optimization (Paperback, Softcover reprint of the original 1st ed. 2003)
Series: Nonconvex Optimization and Its Applications, 72
Expected to ship within 10 - 15 working days
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The field of global optimization has been developing at a rapid
pace. There is a journal devoted to the topic, as well as many
publications and notable books discussing various aspects of global
optimization. This book is intended to complement these other
publications with a focus on stochastic methods for global
optimization. Stochastic methods, such as simulated annealing and
genetic algo rithms, are gaining in popularity among practitioners
and engineers be they are relatively easy to program on a computer
and may be cause applied to a broad class of global optimization
problems. However, the theoretical performance of these stochastic
methods is not well under stood. In this book, an attempt is made
to describe the theoretical prop erties of several stochastic
adaptive search methods. Such a theoretical understanding may allow
us to better predict algorithm performance and ultimately design
new and improved algorithms. This book consolidates a collection of
papers on the analysis and de velopment of stochastic adaptive
search. The first chapter introduces random search algorithms.
Chapters 2-5 describe the theoretical anal ysis of a progression of
algorithms. A main result is that the expected number of iterations
for pure adaptive search is linear in dimension for a class of
Lipschitz global optimization problems. Chapter 6 discusses
algorithms, based on the Hit-and-Run sampling method, that have
been developed to approximate the ideal performance of pure random
search. The final chapter discusses several applications in
engineering that use stochastic adaptive search methods."
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