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Artificial Neural Nets and Genetic Algorithms

Proceedings of the International Conference in Innsbruck, Austria, 1993

  • Conference proceedings
  • © 1993

Overview

Editors:
  1. Rudolf F. Albrecht

    1. Institut für Informatik, Universität Innsbruck, Innsbruck, Austria

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  2. Colin R. Reeves

    1. Division of Statistics and Operational Research, UK

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  3. Nigel C. Steele

    1. Division of Mathematics School of Mathematical and Information Sciences, Coventry University, Coventry, UK

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Table of contents (106 papers)

  1. Artificial Neural Networks & Genetic Algorithms

    1. Genetic Algorithm Design of Neural Net Based Electronic Nose

      • Adhanom A. Fekadu, Evor L. Hines, Julian W. Gardner
      Pages 691-698

    2. Circuits of Production Rule GenNets

      • Hugo de Garis
      Pages 699-705

    3. Neuromimetic Algorithms Processing: Tools for Design of Dedicated Architectures

      • Laurent Kwiatkowski, Jean-Paul Stromboni
      Pages 706-711

    4. Towards the Development of Cognitive Maps in Classifier Systems

      • N. R. Ball
      Pages 712-718

    5. Genetic Optimisation of Neural Network Architectures for Colour Recipe Prediction

      • J. M. Bishop, M. J. Bushnell, A. Usher, S. Westland
      Pages 719-725

    6. Use of Genetic Algorithms for Optimal Topology Determination in Back Propagation Neural Networks

      • Philip Robbins, Alan Soper, Keith Rennolls
      Pages 726-730

    7. Counting and Naming Connection Islands on a Grid of Conductors

      • Daniel C. Fielder, Cecil O. Alford
      Pages 731-737

  2. Back Matter

    Pages 739-741
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Keywords

About this book

Artificial neural networks and genetic algorithms both are areas of research which have their origins in mathematical models constructed in order to gain understanding of important natural processes. By focussing on the process models rather than the processes themselves, significant new computational techniques have evolved which have found application in a large number of diverse fields. This diversity is reflected in the topics which are the subjects of contributions to this volume. There are contributions reporting theoretical developments in the design of neural networks, and in the management of their learning. In a number of contributions, applications to speech recognition tasks, control of industrial processes as well as to credit scoring, and so on, are reflected. Regarding genetic algorithms, several methodological papers consider how genetic algorithms can be improved using an experimental approach, as well as by hybridizing with other useful techniques such as tabu search. The closely related area of classifier systems also receives a significant amount of coverage, aiming at better ways for their implementation. Further, while there are many contributions which explore ways in which genetic algorithms can be applied to real problems, nearly all involve some understanding of the context in order to apply the genetic algorithm paradigm more successfully. That this can indeed be done is evidenced by the range of applications covered in this volume.

Editors and Affiliations

  • Institut für Informatik, Universität Innsbruck, Innsbruck, Austria

    Rudolf F. Albrecht

  • Division of Statistics and Operational Research, UK

    Colin R. Reeves

  • Division of Mathematics School of Mathematical and Information Sciences, Coventry University, Coventry, UK

    Nigel C. Steele

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