Exploitation of Linkage Learning in Evolutionary Algorithms

  • Ying-ping Chen

Part of the Evolutionary Learning and Optimization book series (ALO, volume 3)

Table of contents

  1. Front Matter
  2. Linkage and Problem Structures

    1. Front Matter
      Pages 1-1
    2. Chalermsub Sangkavichitr, Prabhas Chongstitvatana
      Pages 25-44
    3. Alexander E. I. Brownlee, John A. W. McCall, Siddhartha K. Shakya, Qingfu Zhang
      Pages 45-69
    4. Siddhartha Shakya, Alexander Brownlee, John McCall, François Fournier, Gilbert Owusu
      Pages 71-93
  3. Model Building and Exploiting

    1. Front Matter
      Pages 95-95
    2. David Iclănzan, D. Dumitrescu, Béat Hirsbrunner
      Pages 97-122
    3. Li-Fang Wang, Jian-Chao Zeng
      Pages 139-162
    4. Carlos Echegoyen, Alexander Mendiburu, Roberto Santana, Jose A. Lozano
      Pages 163-189
  4. Applications

  5. Back Matter

About this book

Introduction

One major branch of enhancing the performance of evolutionary algorithms is the exploitation of linkage learning. This monograph aims to capture the recent progress of linkage learning, by compiling a series of focused technical chapters to keep abreast of the developments and trends in the area of linkage. In evolutionary algorithms, linkage models the relation between decision variables with the genetic linkage observed in biological systems, and linkage learning connects computational optimization methodologies and natural evolution mechanisms. Exploitation of linkage learning can enable us to design better evolutionary algorithms as well as to potentially gain insight into biological systems. Linkage learning has the potential to become one of the dominant aspects of evolutionary algorithms; research in this area can potentially yield promising results in addressing the scalability issues.

Keywords

Bayesian network Evolutionary Computation Linkage Learning Markov algorithm algorithms calculus evolution evolutionary algorithm genetics knowledge learning model optimization

Editors and affiliations

  • Ying-ping Chen
    • 1
  1. 1.Natural Computing Laboratory Department of Computer ScienceNational Chiao Tung UniversityHsinChu CityTaiwan

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-12834-9
  • Copyright Information Springer-Verlag Berlin Heidelberg 2010
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-12833-2
  • Online ISBN 978-3-642-12834-9
  • Series Print ISSN 1867-4534
  • Series Online ISSN 1867-4542
  • About this book