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Artificial Neural Nets and Genetic Algorithms

Proceedings of the International Conference in Alès, France, 1995

  • Conference proceedings
  • © 1995

Overview

Authors:
  1. David W. Pearson

    1. Laboratoire de Génie Informatique et Ingénierie de Production, Ecole pour les Etudes et la Recherche en Informatique et Electronique (EMA-EERIE), Nîmes, France

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  2. Nigel C. Steele

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

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  3. Rudolf F. Albrecht

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

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Table of contents (134 papers)

  1. Front Matter

    Pages I-XV
    Download chapter PDF

  2. Workshop Summary

    1. Workshop Summary

      • David W. Pearson, Nigel C. Steele, Rudolf F. Albrecht
      Pages 1-2

  5. Classification

    1. A Genetic Algorithm for Multicriteria Inventory Classification

      • H. Altay Güvenir
      Pages 6-9

    2. Optimizing Classifiers for Handwritten Digits by Genetic Algorithms

      • J. Schäfer, H. Braun
      Pages 10-13

    3. DCS: A Promising Classifier System

      • Philippe Collard, Cathy Escazut
      Pages 14-17

    4. Application of neural networks for classification of temperature distribution patterns

      • Henrik Saxén, Abhay Bulsari, Leif Karilainen, Kalevi Raipala
      Pages 18-21

  6. Genetic Algorithms and Combinatorial Optimisation

    1. Combination of Genetic Algorithms and CLP in the Vehicle-Fleet Scheduling Problem

      • E. Stefanitsis, N. Christodoulou, J. Psarras
      Pages 22-25

    2. Minimum Cost Topology Optimisation of the Cost 239 European Optical Network

      • M. C. Sinclair
      Pages 26-29

    3. Timetabling Using Genetic Algorithms

      • Wilhelm Erben
      Pages 30-32

    4. Resolution of Cartographic Layout Problem by Means of Improved Genetic Algorithms

      • Yassine Djouadi
      Pages 33-36

    5. Using Genetic Algorithms to Solve the Radio Link Frequency Assignment Problem

      • A. Kapsalis, V. J. Rayward-Smith, G. D. Smith
      Pages 37-40

  7. Learning and Training

    1. A Transformation for Implementing Efficient Dynamic Backpropagation Neural Networks

      • George L. Rudolph, Tony R. Martinez
      Pages 41-44

    2. AA1*: A Dynamic Incremental Network that Learns by Discrimination

      • Christophe Giraud-Carrier, Tony Martinez
      Pages 45-48

    3. Non-Supervised Sensory-Motor Agents Learning

      • Raul Sidnei Wazlawick, Antonio Carlos da Rocha Costa
      Pages 49-52

    4. Functional Equivalence and Genetic Learning of RBF Networks

      • Roman Neruda
      Pages 53-56

    5. Teaching Relaxation Labeling Processes Using Genetic Algorithms

      • Marcello Pelillo, Fabio Abbattista, Angelo Maffione
      Pages 57-60

    6. VLSI Optimal Neural Network Learning Algorithm

      • Valeriu Beiu, John G. Taylor
      Pages 61-64

  8. Applications in Biology and Biotechnology

    1. Using of Neural-Network and Expert System for Imissions Prediction

      • E. Theocharidou, Z. Burianec
      Pages 65-68
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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 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.

Authors and Affiliations

  • Laboratoire de Génie Informatique et Ingénierie de Production, Ecole pour les Etudes et la Recherche en Informatique et Electronique (EMA-EERIE), Nîmes, France

    David W. Pearson

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

    Nigel C. Steele

  • Institut für Informatik, Universität Innsbruck, Innsbruck, Austria

    Rudolf F. Albrecht

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