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Foundations of Global Genetic Optimization

  • Robert Schaefer

Part of the Studies in Computational Intelligence book series (SCI, volume 74)

Table of contents

  1. Front Matter
    Pages I-XI
  2. Robert Schaefer
    Pages 1-6
  3. Robert Schaefer
    Pages 7-30
  4. Robert Schaefer
    Pages 31-53
  5. Robert Schaefer
    Pages 115-152
  6. Back Matter
    Pages 203-222

About this book

Introduction

This book is devoted to the application of genetic algorithms in continuous global optimization. Some of their properties and behavior are highlighted and formally justified. Various optimization techniques and their taxonomy are the background for detailed discussion. The nature of continuous genetic search is explained by studying the dynamics of probabilistic measure, which is utilized to create subsequent populations. This approach shows that genetic algorithms can be used to extract some areas of the search domain more effectively than to find isolated local minima. The biological metaphor of such behavior is the whole population surviving by rapid exploration of new regions of feeding rather than caring for a single individual. One group of strategies that can make use of this property are two-phase global optimization methods. In the first phase the central parts of the basins of attraction are distinguished by genetic population analysis. Afterwards, the minimizers are found by convex optimization methods executed in parallel.

Keywords

Artificial Genetic Systems Clustered Genetic Search adaptation algorithm algorithms behavior genetic algorithms global optimization optimization

Authors and affiliations

  • Robert Schaefer
    • 1
  1. 1.AGH University of Science and Technology30-059KrakówPoland

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-73192-4
  • Copyright Information Springer Berlin Heidelberg 2007
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-73191-7
  • Online ISBN 978-3-540-73192-4
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
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