Table of contents

  1. Front Matter
  2. Janez Brest, Aleš Zamuda, Borko Bošković, Sašo Greiner, Viljem Žumer
    Pages 89-110
  3. Shahryar Rahnamayan, Hamid R. Tizhoosh, Magdy M. A. Salama
    Pages 155-171
  4. Efrén Mezura-Montes, Margarita Reyes-Sierra, Carlos A. Coello Coello
    Pages 173-196
  5. V. P. Plagianakos, D. K. Tasoulis, M. N. Vrahatis
    Pages 197-238
  6. A. Massa, M. Pastorino, A. Randazzo
    Pages 239-255
  7. L. Lakshminarasimman, S. Subramanian
    Pages 257-273
  8. Borko Bošković, Sašo Greiner, Janez Brest, Aleš Zamuda, Viljem Žumer
    Pages 287-298
  9. Rui Mendes, Isabel Rocha, José P. Pinto, Eugénio C. Ferreira, Miguel Rocha
    Pages 299-317
  10. P. P. Menon, D. G. Bates, I. Postlethwaite, A. Marcos, V. Fernandez, S. Bennani
    Pages 319-333
  11. Back Matter

About this book

Introduction

Differential evolution is arguably one of the hottest topics in today's computational intelligence research. This book seeks to present a comprehensive study of the state of the art in this technology and also directions for future research.

The fourteen chapters of this book have been written by leading experts in the area. The first seven chapters focus on algorithm design, while the last seven describe real-world applications. Chapter 1 introduces the basic differential evolution (DE) algorithm and presents a broad overview of the field. Chapter 2 presents a new, rotationally invariant DE algorithm. The role of self-adaptive control parameters in DE is investigated in Chapter 3. Chapters 4 and 5 address constrained optimization; the former develops suitable stopping conditions for the DE run, and the latter presents an improved DE algorithm for problems with very small feasible regions. A novel DE algorithm, based on the concept of "opposite" points, is the topic of Chapter 6. Chapter 7 provides a survey of multi-objective differential evolution algorithms. A review of the major application areas of differential evolution is presented in Chapter 8. Chapter 9 discusses the application of differential evolution in two important areas of applied electromagnetics. Chapters 10 and 11 focus on applications of hybrid DE algorithms to problems in power system optimization. Chapter 12 applies the DE algorithm to computer chess. The use of DE to solve a problem in bioprocess engineering is discussed in Chapter 13. Chapter 14 describes the application of hybrid differential evolution to a problem in control engineering.

Keywords

Analysis algorithm algorithms computational intelligence computer chess control engineering evolution intelligence multi-objective optimization optimization process engineering

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-68830-3
  • Copyright Information Springer-Verlag Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-68827-3
  • Online ISBN 978-3-540-68830-3
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book