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Advances in Evolutionary Computing

Theory and Applications

  • Book
  • © 2003

Overview

Editors:
  1. Ashish Ghosh

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

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  2. Shigeyoshi Tsutsui

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

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    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)

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Table of contents (40 chapters)

  1. Front Matter

    Pages I-XVI
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  2. Theory

    1. Front Matter

      Pages 1-1
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    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

    7. Multiparent Recombination in Evolutionary Computing

      • A. E. Eiben
      Pages 175-192

    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

    11. Multi-objective Evolutionary Algorithms: Introducing Bias Among Pareto-optimal Solutions

      • Kalyanmoy Deb
      Pages 263-292

    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

    14. Effects of Adding Perturbations to Phenotypic Parameters in Genetic Algorithms for Searching Robust Solutions

      • Shigeyoshi Tsutsui, Ashish Ghosh
      Pages 351-365

    15. Evolution of Strategies for Resource Protection Problems

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

    16. A Unified Bayesian Framework for Evolutionary Learning and Optimization

      • Byoung-Tak Zhang
      Pages 393-412

    17. Designed Sampling with Crossover Operators

      • Akiko Aizawa
      Pages 413-439

    18. Evolutionary Computation for Evolutionary Theory

      • C. C. Maley
      Pages 441-460

    19. Computational Embryology: Past, Present and Future

      • Sanjeev Kumar, Peter J. Bentley
      Pages 461-477
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Keywords

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).

Editors and Affiliations

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

    Ashish Ghosh

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

    Shigeyoshi Tsutsui

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