Adaptive Learning by Genetic Algorithms

Released on by Herbert Dawid
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  • Adaptive Learning by Genetic Algorithms Book Detail

  • Author : Herbert Dawid
  • Release Date : 2011-06-28
  • Publisher : Springer Science & Business Media
  • Genre : Mathematics
  • Pages : 203
  • ISBN 13 : 3642181422
  • File Size : 71,71 MB
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Adaptive Learning by Genetic Algorithms by Herbert Dawid PDF Summary

Book Description: The fact that I have the opportunity to present a second edition of this monograph is an indicator for the growing size of the community concerned with agent-based computational economics. The rapid developments in this field make it very difficult to keep a volume like this, which is partly devoted to surveying the literature, up to date. I have done my best to incorporate the relevant new developments in this revised edition but it is in the nature of such a work that the selection of material covered is biased by the authors personal interest and his informational constraints. My apologies go to all researchers in this field whose work is not or not adequately represented in this book. Besides the correction of some errors and typos several additions have been made. In the literature survey sections 2.4 (which was also reorganized) and 3.5 new material was added. I have also added a new section in chapter 3 which deals with the question how well empirically observed phenomena can be explained by GA simulations. A new section in chapter 6 presents a rather extensive analysis of the behavior of a two population GA in the framework of a sealed bid double auction market. Further minor additions and changes were made throughout the text.

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Adaptive Learning by Genetic Algorithms

Adaptive Learning by Genetic Algorithms

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The fact that I have the opportunity to present a second edition of this monograph is an indicator for the growing size of the community concerned with agent-ba

Adaptive Learning by Genetic Algorithms

Adaptive Learning by Genetic Algorithms

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Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolut