Fuzzy Evolutionary Computation

  • Witold Pedrycz

Table of contents

  1. Front Matter
    Pages i-xv
  2. Fundamentals

    1. Front Matter
      Pages 1-1
    2. Zbigniew Michalewicz, Robert Hinterding, Maciej Michalewicz
      Pages 3-31
  3. Methodology and Algorithms

    1. Front Matter
      Pages 79-79
    2. Toshio Fukuda, Naoyuki Kubota, Takemasa Arakawa
      Pages 81-105
    3. Hisao Ishibuchi, Tadahiko Murata, Tomoharu Nakashima
      Pages 127-153
    4. Derek A. Linkens, H. Okola Nyongesa
      Pages 199-222
    5. Luis Magdalena, Juan R. Velasco
      Pages 249-268
    6. Oliver Nelles
      Pages 269-295
  4. Bibliography

    1. Front Matter
      Pages 297-297
  5. Back Matter
    Pages 319-320

About this book

Introduction

As of today, Evolutionary Computing and Fuzzy Set Computing are two mature, wen -developed, and higbly advanced technologies of information processing. Bach of them has its own clearly defined research agenda, specific goals to be achieved, and a wen setUed algorithmic environment. Concisely speaking, Evolutionary Computing (EC) is aimed at a coherent population -oriented methodology of structural and parametric optimization of a diversity of systems. In addition to this broad spectrum of such optimization applications, this paradigm otTers an important ability to cope with realistic goals and design objectives reflected in the form of relevant fitness functions. The GA search (which is often regarded as a dominant domain among other techniques of EC such as evolutionary strategies, genetic programming or evolutionary programming) delivers a great deal of efficiency helping navigate through large search spaces. The main thrust of fuzzy sets is in representing and managing nonnumeric (linguistic) information. The key notion (whose conceptual as weH as algorithmic importance has started to increase in the recent years) is that of information granularity. It somewhat concurs with the principle of incompatibility coined by L. A. Zadeh. Fuzzy sets form a vehic1e helpful in expressing a granular character of information to be captured. Once quantified via fuzzy sets or fuzzy relations, the domain knowledge could be used efficiently very often reducing a heavy computation burden when analyzing and optimizing complex systems.

Keywords

algorithms classification evolution evolutionary algorithm evolutionary computation fuzzy genetic algorithm genetic algorithms intelligent systems knowledge learning optimization robot sets

Editors and affiliations

  • Witold Pedrycz
    • 1
  1. 1.The University of ManitobaWinnipegCanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4615-6135-4
  • Copyright Information Kluwer Academic Publishers 1997
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-7811-2
  • Online ISBN 978-1-4615-6135-4
  • About this book