Stochastic Adaptive Search for Global Optimization (Softcover reprint of the original 1st ed. 2003)

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.