Recent Advances in Memetic Algorithms

  • William E. Hart
  • J. E. Smith
  • N. Krasnogor
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 166)

Table of contents

  1. Front Matter
    Pages I-X
  2. Introduction to Memetic Algorithms

    1. Front Matter
      Pages 1-1
    2. W. E. Hart, N. Krasnogor, J. E. Smith
      Pages 3-27
  3. Applications of Memetic Algorithms

  4. Methodological Aspects of Memetic Algorithms

    1. Front Matter
      Pages 183-183
    2. Natalio Krasnogor
      Pages 185-207
    3. Natalio Krasnogor, Steven Gustafson
      Pages 229-257
    4. Abhishek Sinha, Ying-ping Chen, David E. Goldberg
      Pages 259-288
  5. Related Search Technologies

    1. Front Matter
      Pages 353-353

About this book

Introduction

Memetic algorithms are evolutionary algorithms that apply a local search process to refine solutions to hard problems. Memetic algorithms are the subject of intense scientific research and have been successfully applied to a multitude of real-world problems ranging from the construction of optimal university exam timetables, to the prediction of protein structures and the optimal design of space-craft trajectories. "Recent Advances in Memetic Algorithms" presents a rich state-of-the-art gallery of works on Memetic algorithms. Recent Advances in Memetic Algorithms is the first book that focuses on this technology as the central topical matter.  This monograph gives a coherent, integrated view on both good practice examples and new trends including a concise and self-contained introduction to Memetic Algorithms. It is a necessary read for postgraduate students and researchers interested in recent advances in search and optimization technologies based on Memetic algorithms, but can also be used as complement to undergraduate textbooks on artificial intelligence.

Keywords

algorithm algorithms artificial intelligence best fit combinatorial optimization communication construction evolutionary algorithm fuzzy intelligence learning operator optimization virtual reality

Editors and affiliations

  • William E. Hart
    • 1
  • J. E. Smith
    • 2
  • N. Krasnogor
    • 3
  1. 1.Algorithms and Discrete Mathematics DepartmentSandia National LaboratoriesAlbuquerqueUSA
  2. 2.Faculty of Computing, Engineering and Mathematical SciencesUniversity of the West of EnglandBristolUK
  3. 3.Automatic Scheduling, Optimisation and Planning Group, School of Computer Science and ITUniversity of NottinghamNottinghamUK

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-32363-5
  • Copyright Information Springer-Verlag Berlin Heidelberg 2005
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-22904-9
  • Online ISBN 978-3-540-32363-1
  • Series Print ISSN 1434-9922
  • About this book