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Summary

  • A. E. Eiben
  • J. E. Smith
Part of the Natural Computing Series book series (NCS)

Abstract

In this book we have presented evolutionary computing as one problem-solving paradigm and positioned four historical types of EAs as “dialects”. These dialects have emerged independently to some extent (except GP that grew out of GAs) and developed their own terminology, research focus, and technical solutions to realise particular evolutionary algorithm features. The differences between them, however, are not crisp — there are many examples of EAs that are hard to place into one of the historical categories. Thus after the introductory material in Chaps. 1 and 2, we proceeded to describe these four main dialects of EC in Chaps. 3–6. In Table 15.1 we give a brief summary of these dialects.

Keywords

Constraint Satisfaction Problem Artificial Immune System Evolutionary Computing Evolutionary Robotic Historical Category 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • A. E. Eiben
    • 1
  • J. E. Smith
    • 2
  1. 1.Faculty of SciencesVrije Universiteit AmsterdamAmsterdamThe Netherlands
  2. 2.Faculty of Computing, Engineering and Mathematical SciencesUniversity of the West of EnglandBristolUK

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