Stochastic Adaptive Search for Global Optimization

  • Zelda B. Zabinsky

Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 72)

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Zelda B. Zabinsky
    Pages 1-23
  3. Zelda B. Zabinsky
    Pages 25-54
  4. Zelda B. Zabinsky
    Pages 55-81
  5. Zelda B. Zabinsky
    Pages 83-104
  6. Zelda B. Zabinsky
    Pages 105-128
  7. Zelda B. Zabinsky
    Pages 129-176
  8. Zelda B. Zabinsky
    Pages 177-208
  9. Back Matter
    Pages 209-224

About this book

Introduction

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.

Keywords

algorithms complexity global optimization operations research optimization

Authors and affiliations

  • Zelda B. Zabinsky
    • 1
  1. 1.University of WashingtonSeattleUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4419-9182-9
  • Copyright Information Springer-Verlag US 2003
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-4826-9
  • Online ISBN 978-1-4419-9182-9
  • Series Print ISSN 1571-568X
  • About this book