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  • © 2008

Linkage in Evolutionary Computation

  • Presents recent results in Linkage in Evolutionary Computation

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

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Table of contents (19 chapters)

  1. Front Matter

  2. Models and Theories

    1. Front Matter

      Pages 1-1
    2. Linkage in Island Models

      • Zbigniew Skolicki
      Pages 41-60
    3. Real-Coded ECGA for Solving Decomposable Real-Valued Optimization Problems

      • Minqiang Li, David E. Goldberg, Kumara Sastry, Tian-Li Yu
      Pages 61-86
    4. Linkage Learning Accuracy in the Bayesian Optimization Algorithm

      • Claudio F. Lima, Martin Pelikan, David E. Goldberg, Fernando G. Lobo, Kumara Sastry, Mark Hauschild
      Pages 87-107
    5. The Impact of Exact Probabilistic Learning Algorithms in EDAs Based on Bayesian Networks

      • Carlos Echegoyen, Roberto Santana, Jose A. Lozano, Pedro Larrañaga
      Pages 109-139
    6. Linkage Learning in Estimation of Distribution Algorithms

      • David Coffin, Robert E. Smith
      Pages 141-156
  3. Operators and Frameworks

    1. Front Matter

      Pages 157-157
    2. Parallel GEAs with Linkage Analysis over Grid

      • Asim Munawar, Mohamed Wahib, Masaharu Munetomo, Kiyoshi Akama
      Pages 159-187
    3. Identification and Exploitation of Linkage by Means of Alternative Splicing

      • Philipp Rohlfshagen, John A. Bullinaria
      Pages 189-223
    4. Studying the Effects of Dual Coding on the Adaptation of Representation for Linkage in Evolutionary Algorithms

      • Maroun Bercachi, Philippe Collard, Manuel Clergue, Sebastien Verel
      Pages 249-284
    5. Symbiotic Evolution to Avoid Linkage Problem

      • Ramin Halavati, Saeed Bagheri Shouraki
      Pages 285-314
    6. EpiSwarm, a Swarm-Based System for Investigating Genetic Epistasis

      • Thomas Goth, Chia-Ti Tsai, Fu-Tien Chiang, Clare Bates Congdon
      Pages 315-334
    7. Real-Coded Extended Compact Genetic Algorithm Based on Mixtures of Models

      • Pier Luca Lanzi, Luigi Nichetti, Kumara Sastry, Davide Voltini, David E. Goldberg
      Pages 335-358
  4. Applications

    1. Front Matter

      Pages 359-359
    2. A Decomposed Approach for the Minimum Interference Frequency Assignment

      • 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.

Bibliographic Information

Buy it now

Buying options

eBook USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access