© 2008

Linkage in Evolutionary Computation

  • Editors
  • Ying-ping Chen
  • Meng-Hiot Lim

Part of the Studies in Computational Intelligence book series (SCI, volume 157)

Table of contents

  1. Front Matter
  2. Models and Theories

    1. Front Matter
      Pages 1-1
    2. Zbigniew Skolicki
      Pages 41-60
    3. Minqiang Li, David E. Goldberg, Kumara Sastry, Tian-Li Yu
      Pages 61-86
    4. Claudio F. Lima, Martin Pelikan, David E. Goldberg, Fernando G. Lobo, Kumara Sastry, Mark Hauschild
      Pages 87-107
    5. Carlos Echegoyen, Roberto Santana, Jose A. Lozano, Pedro Larrañaga
      Pages 109-139
    6. David Coffin, Robert E. Smith
      Pages 141-156
  3. Operators and Frameworks

    1. Front Matter
      Pages 157-157
    2. Asim Munawar, Mohamed Wahib, Masaharu Munetomo, Kiyoshi Akama
      Pages 159-187
    3. Philipp Rohlfshagen, John A. Bullinaria
      Pages 189-223
    4. Maroun Bercachi, Philippe Collard, Manuel Clergue, Sebastien Verel
      Pages 249-284
    5. Ramin Halavati, Saeed Bagheri Shouraki
      Pages 285-314
    6. Thomas Goth, Chia-Ti Tsai, Fu-Tien Chiang, Clare Bates Congdon
      Pages 315-334
    7. Pier Luca Lanzi, Luigi Nichetti, Kumara Sastry, Davide Voltini, David E. Goldberg
      Pages 335-358
  4. Applications

    1. Front Matter
      Pages 359-359
    2. Gualtiero Colombo, Stuart M. Allen
      Pages 389-417

About this book


In recent years, the issue of linkage in GEAs has garnered greater attention and recognition from researchers. Conventional approaches that rely much on ad hoc tweaking of parameters to control the search by balancing the level of exploitation and exploration are grossly inadequate. As shown in the work reported here, such parameters tweaking based approaches have their limits; they can be easily ”fooled” by cases of triviality or peculiarity of the class of problems that the algorithms are designed to handle. Furthermore, these approaches are usually blind to the interactions between the decision variables, thereby disrupting the partial solutions that are being built up along the way.

The whole volume consisting of 19 chapters is divided into 3 parts: Models and Theories; Operators and Frameworks; Applications. This edited volume will serve as a useful guide and reference for researchers who are currently working in the area of linkage. For postgraduate research students, this volume will serve as a good source of reference. It is also suitable as a text for a graduate level course focusing on linkage issues. For practitioners who are looking at putting into practice the concept of linkage, the few chapters on applications will serve as a useful guide.


Bayesian network Evolution Evolutionary Computation Linkage Operator algorithm algorithms calculus cognition evolutionary algorithm genetic algorithms learning microelectromechanical system (MEMS) model optimization

Bibliographic information