Stochastic Adaptive Search for Global Optimization
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 shastic methods for global optimization. Shastic methods, such as simulated annealing and genetic algorithms, 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 shastic methods is not well under stood. In this book, an attempt is made to describe the theoretical properties of several shastic 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 development of shastic adaptive search. The first chapter introduces random search algorithms. Chapters 2-5 describe the theoretical analysis 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 shastic adaptive search methods.
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Stochastic Adaptive Search for Global Optimization
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 shastic methods for global optimization. Shastic methods, such as simulated annealing and genetic algorithms, 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 shastic methods is not well under stood. In this book, an attempt is made to describe the theoretical properties of several shastic 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 development of shastic adaptive search. The first chapter introduces random search algorithms. Chapters 2-5 describe the theoretical analysis 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 shastic adaptive search methods.
109.99
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Stochastic Adaptive Search for Global Optimization
224
Stochastic Adaptive Search for Global Optimization
224Paperback(Softcover reprint of the original 1st ed. 2003)
$109.99
109.99
In Stock
Product Details
| ISBN-13: | 9781461348269 |
|---|---|
| Publisher: | Springer US |
| Publication date: | 11/20/2013 |
| Series: | Nonconvex Optimization and Its Applications , #72 |
| Edition description: | Softcover reprint of the original 1st ed. 2003 |
| Pages: | 224 |
| Product dimensions: | 6.10(w) x 9.25(h) x 0.02(d) |
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