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Artificial Neural Nets and Genetic Algorithms

Proceedings of the International Conference in Alès, France, 1995

  • David W. Pearson
  • Nigel C. Steele
  • Rudolf F. Albrecht

Table of contents

  1. Front Matter
    Pages I-XV
  2. Workshop Summary

    1. David W. Pearson, Nigel C. Steele, Rudolf F. Albrecht
      Pages 1-2
  3. Invited Talk

  4. Plenary Session

  5. Classification

    1. Philippe Collard, Cathy Escazut
      Pages 14-17
    2. Henrik Saxén, Abhay Bulsari, Leif Karilainen, Kalevi Raipala
      Pages 18-21
  6. Genetic Algorithms and Combinatorial Optimisation

  7. Learning and Training

    1. Christophe Giraud-Carrier, Tony Martinez
      Pages 45-48
    2. Raul Sidnei Wazlawick, Antonio Carlos da Rocha Costa
      Pages 49-52
    3. Marcello Pelillo, Fabio Abbattista, Angelo Maffione
      Pages 57-60
    4. Valeriu Beiu, John G. Taylor
      Pages 61-64
  8. Applications in Biology and Biotechnology

    1. M. Leck, P. Bromley, D. Peel, A. M. Gerrard
      Pages 69-71
    2. P. Koprinkova, M. Petrova, T. Patarinska
      Pages 72-75
    3. A. J. F. Coimbra, J. Marino-Neto, F. M. de Azevedo, C. G. Freitas, J. M. Barreto
      Pages 76-79
  9. Genetic and Neural Theory Combined

    1. Olivier Michel, Joëlle Biondi
      Pages 80-83
    2. Hugo de Garis
      Pages 84-87
    3. J. J. Merelo, A. Prieto
      Pages 92-95
  10. Failure Detection and Identification

  11. Image, Motion and Recognition

    1. Ernst M. Kussul, Tatyana N. Baidyk
      Pages 120-123
    2. B. Martín-del-Brío, N. Medrano-Marqués, J. Blasco-Alberto
      Pages 124-127
    3. R. M. Rylatt, C. A. Czarnecki, T. W. Routen
      Pages 128-131
    4. Stéphane Durand, Frédéric Alexandre
      Pages 132-135
    5. Olaf Schnelting, Bernd Michaelis, Rüdiger Mecke
      Pages 136-139
    6. Sławomir Skoneczny, Jarosław Szostakowski
      Pages 140-143
  12. Genetic Algorithms Theory

    1. David Reynolds, Jagannathan Gomatam
      Pages 148-151
    2. J. Holmes, T. W. Routen, C. A. Czarnecki
      Pages 156-159
    3. Cathy Escazut, Philippe Collard
      Pages 160-163
  13. Neural Networks Theory

About these proceedings

Introduction

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 subjects of the contributions to this volume. There are contributions reporting successful applications of the technology to the solution of industrial/commercial problems. This may well reflect the maturity of the technology, notably in the sense that 'real' users of modelling/prediction techniques are prepared to accept neural networks as a valid paradigm. Theoretical issues also receive attention, notably in connection with the radial basis function neural network. Contributions in the field of genetic algorithms reflect the wide range of current applications, including, for example, portfolio selection, filter design, frequency assignment, tuning of nonlinear PID controllers. These techniques are also used extensively for combinatorial optimisation problems.

Keywords

agents algorithms artificial neural network control fuzzy logic genetic algorithms learning modeling multimedia networks neural networks optimization pattern recognition simulation speech recognition

Authors and affiliations

  • David W. Pearson
    • 1
  • Nigel C. Steele
    • 2
  • Rudolf F. Albrecht
    • 3
  1. 1.Laboratoire de Génie Informatique et Ingénierie de ProductionEcole pour les Etudes et la Recherche en Informatique et Electronique (EMA-EERIE)NîmesFrance
  2. 2.Division of Mathematics School of Mathematical and Information SciencesCoventry UniversityCoventryUK
  3. 3.Institut für InformatikUniversität InnsbruckInnsbruckAustria

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