Evolutionary Computation in Dynamic and Uncertain Environments

  • Shengxiang Yang
  • Yew-Soon Ong
  • Yaochu Jin

Part of the Studies in Computational Intelligence book series (SCI, volume 51)

Table of contents

  1. Front Matter
    Pages I-XXIII
  2. Optimum Tracking in Dynamic Environments

    1. Front Matter
      Pages 1-1
    2. Lutz Schönemann
      Pages 51-77
    3. Sanyou Zeng, Hui Shi, Lishan Kang, Lixin Ding
      Pages 79-104
    4. Hai H. Dam, Chris Lokan, Hussein A. Abbass
      Pages 153-178
    5. Zbigniew Michalewicz, Martin Schmidt, Matthew Michalewicz, Constantin Chiriac
      Pages 179-196
    6. Frederico Paiva Quintão, Fabíola Guerra Nakamura, Geraldo Robson Mateus
      Pages 197-222
  3. Approximation of Fitness Functions

  4. Handling Noisy Fitness Functions

    1. Front Matter
      Pages 344-344
    2. Ferrante Neri, Raino A. E. Mäkinen
      Pages 345-369
    3. Kagan Tumer, Adrian Agogino
      Pages 371-387

About this book

Introduction

This book provides a compilation on the state-of-the-art and recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The motivation for this book arises from the fact that some degree of uncertainty in characterizing any realistic engineering systems is inevitable. Representative methods for addressing major sources of uncertainties in evolutionary computation, including handle of noisy fitness functions, use of approximate fitness functions, search for robust solutions, and tracking moving optimums, are presented. "Evolutionary Computation in Dynamic and Uncertain Environments" is a valuable reference for scientists, researchers, professionals and students in the field of engineering and science, particularly in the areas of computational intelligence, natural computing and evolutionary computation.

Keywords

algorithm algorithms artificial intelligence data mining evolution evolutionary algorithm evolutionary computation evolutionary strategies genetic algorithms genetic programming intelligence modeling neural networks optimization uncertainty

Editors and affiliations

  • Shengxiang Yang
    • 1
  • Yew-Soon Ong
    • 2
  • Yaochu Jin
    • 3
  1. 1.Department of Computer ScienceUniversity of LeicesterLeicesteUK
  2. 2.School of Computer EngineeringNanyang Technological UniversitySingapore
  3. 3.Honda Research Institute EuropeOffenbach am MainGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-49774-5
  • Copyright Information Springer Berlin Heidelberg 2007
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-49772-1
  • Online ISBN 978-3-540-49774-5
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book