Multi-Objective Memetic Algorithms

  • Chi-Keong Goh
  • Yew-Soon Ong
  • Kay Chen Tan

Part of the Studies in Computational Intelligence book series (SCI, volume 171)

Table of contents

  1. Front Matter
  2. Introduction

    1. Front Matter
      Pages 1-1
    2. Hisao Ishibuchi, Yasuhiro Hitotsuyanagi, Noritaka Tsukamoto, Yusuke Nojima
      Pages 27-49
  3. Knowledge Infused in Design of Problem-Specific Operators

    1. Front Matter
      Pages 51-51
    2. Zexuan Zhu, Yew-Soon Ong, Jer-Lai Kuo
      Pages 111-131
    3. Koji Shimoyama, Jin Ne Lim, Shinkyu Jeong, Shigeru Obayashi, Masataka Koishi
      Pages 133-151
    4. Chariklia A. Georgopoulou, Kyriakos C. Giannakoglou
      Pages 153-181
    5. Salem F. Adra, Ian Griffin, Peter J. Fleming
      Pages 183-205
  4. Knowledge Propagation through Cultural Evolution

  5. Information Exploited for Local Improvement

    1. Front Matter
      Pages 279-279
    2. Tatsuya Okabe, Yaochu Jin, Bernhard Sendhoff
      Pages 281-307
    3. Tapabrata Ray, Amitay Isaacs, Warren Smith
      Pages 353-367
    4. Omar Soliman, Lam T. Bui, Hussein Abbass
      Pages 369-388
  6. Back Matter

About this book


The application of sophisticated evolutionary computing approaches for solving complex problems with multiple conflicting objectives in science and engineering have increased steadily in the recent years. Within this growing trend, Memetic algorithms are, perhaps, one of the most successful stories, having demonstrated better efficacy in dealing with multi-objective problems as compared to its conventional counterparts. Nonetheless, researchers are only beginning to realize the vast potential of multi-objective Memetic algorithm and there remain many open topics in its design.

This book presents a very first comprehensive collection of works, written by leading researchers in the field, and reflects the current state-of-the-art in the theory and practice of multi-objective Memetic algorithms. "Multi-Objective Memetic algorithms" is organized for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of Memetic algorithms and multi-objective optimization.


algorithm algorithms combinatorial optimization computational intelligence data mining evolution evolutionary algorithm evolutionary computation genetic algorithms heuristics knowledge multi-objective optimization operator optimization simul

Editors and affiliations

  • Chi-Keong Goh
    • 1
  • Yew-Soon Ong
    • 2
  • Kay Chen Tan
    • 1
  1. 1.Department of Electrical and Computer EngineeringNational University of SingaporeSingapore
  2. 2.School of Computer EngineeringNanyang Technological UniversitySingapore

Bibliographic information

  • DOI
  • Copyright Information Springer Berlin Heidelberg 2009
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-540-88050-9
  • Online ISBN 978-3-540-88051-6
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site