Skip to main content
SpringerLink
Log in
Book cover
  • Book
  • © 2003

Advances in Evolutionary Computing

Theory and Applications

Editors:

  1. Ashish Ghosh

    1. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Shigeyoshi Tsutsui

    1. Department of Management Information, Hannan University, Matsubara, Osaka, Japan

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • State of the art of theory and applications in Evolutionary Algorithms Contributions by established researchers in the field
  • Well-balanced between theory and applications
  • Includes supplementary material: sn.pub/extras

Part of the book series: Natural Computing Series (NCS)

Sections

Buy it now

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

This is a preview of subscription content, log in via an institution to check for access.

Table of contents (40 chapters)

  1. Front Matter

    Pages I-XVI
    PDF

  2. Theory

    1. Front Matter

      Pages 1-1
      PDF

    2. Smoothness, Ruggedness and Neutrality of Fitness Landscapes: from Theory to Application

      • Vesselin K. Vassilev, Terence C. Fogarty, Julian F. Miller
      Pages 3-44

    3. Fast Evolutionary Algorithms

      • Xin Yao, Yong Liu, Ko-Hsin Liang, Guangming Lin
      Pages 45-94

    4. Visualizing Evolutionary Computation

      • Trevor D. Collins
      Pages 95-116

    5. New Schemes of Biologically Inspired Evolutionary Computation

      • Hidefumi Sawai, Susumu Adachi, Sachio Kizu
      Pages 117-151

    6. On the Design of Problem-specific Evolutionary Algorithms

      • Stefan Droste, Dirk Wiesmann
      Pages 153-173

    8. TCG-2: A Test-Case Generator for Non-linear Parameter Optimisation Techniques

      • Zbigniew Michalewicz, Martin Schmidt
      Pages 193-212

    9. A Real-coded Genetic Algorithm using the Unimodal Normal Distribution Crossover

      • Isao Ono, Hajime Kita, Shigenobu Kobayashi
      Pages 213-237

    10. Designing Evolutionary Algorithms for Dynamic Optimization Problems

      • Jürgen Branke, Hartmut Schmeck
      Pages 239-262

    12. Gene Expression and Scalable Genetic Search

      • Hillol Kargupta
      Pages 293-319

    13. Solving Permutation Problems with the Ordering Messy Genetic Algorithm

      • Dimitri Knjazew, David E. Goldberg
      Pages 321-350

    15. Evolution of Strategies for Resource Protection Problems

      • William M. Spears, Diana F. Gordon-Spears
      Pages 367-392

    17. Designed Sampling with Crossover Operators

      • Akiko Aizawa
      Pages 413-439

    19. Computational Embryology: Past, Present and Future

      • Sanjeev Kumar, Peter J. Bentley
      Pages 461-477
Back to top

About this book

The term evolutionary computing refers to the study of the foundations and applications of certain heuristic techniques based on the principles of natural evolution; thus the aim of designing evolutionary algorithms (EAs) is to mimic some of the processes taking place in natural evolution. These algo­ rithms are classified into three main categories, depending more on historical development than on major functional techniques. In fact, their biological basis is essentially the same. Hence EC = GA uGP u ES uEP EC = Evolutionary Computing GA = Genetic Algorithms,GP = Genetic Programming ES = Evolution Strategies,EP = Evolutionary Programming Although the details of biological evolution are not completely understood (even nowadays), there is some strong experimental evidence to support the following points: • Evolution is a process operating on chromosomes rather than on organ­ isms. • Natural selection is the mechanism that selects organisms which are well­ adapted to the environment toreproduce more often than those which are not. • The evolutionary process takes place during the reproduction stage that includes mutation (which causes the chromosomes of offspring to be dif­ ferent from those of the parents) and recombination (which combines the chromosomes of the parents to produce the offspring). Based upon these features, the previously mentioned three models of evolutionary computing were independently (and almost simultaneously) de­ veloped. An evolutionary algorithm (EA) is an iterative and stochastic process that operates on a set of individuals (called a population).
Back to top

Keywords

Back to top

Editors and Affiliations

  • Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

    Ashish Ghosh

  • Department of Management Information, Hannan University, Matsubara, Osaka, Japan

    Shigeyoshi Tsutsui

Back to top

Bibliographic Information

Back to top

Publish with us

Policies and ethics

Back to top
Access via your institution

Buy it now

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

Search

Navigation