Artificial Neural Nets and Genetic Algorithms

Proceedings of the International Conference in Portorož, Slovenia, 1999

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

Table of contents

  1. Front Matter
    Pages I-X
  2. Plenary talks

  3. Neural Networks – Theory and Applications

    1. Dan Ventura, Tony Martinez
      Pages 22-27
    2. Alexis Quesada-Arencibia, Miguel Alemán-Flores, Roberto Moreno-Diaz Jr.
      Pages 28-34
    3. David McLean, Zuhair Bandar, Jim O’Shea
      Pages 35-39
    4. Michael Fairbank, Andrew Tuson
      Pages 46-51
    5. Fa-Long Luo, Rolf Unbehauen, Tertulien Ndjountche
      Pages 59-66
    6. Horst Bischof, Aleš Leonardis
      Pages 78-85
    7. Michael Korkin, Hugo de Garis, N. Eiji Nawa, William Dee Rieken
      Pages 107-110

About these proceedings

Introduction

From the contents: Neural networks – theory and applications: NNs (= neural networks) classifier on continuous data domains– quantum associative memory – a new class of neuron-like discrete filters to image processing – modular NNs for improving generalisation properties – presynaptic inhibition modelling for image processing application – NN recognition system for a curvature primal sketch – NN based nonlinear temporal-spatial noise rejection system – relaxation rate for improving Hopfield network – Oja's NN and influence of the learning gain on its dynamics Genetic algorithms – theory and applications: transposition: a biological-inspired mechanism to use with GAs (= genetic algorithms) – GA for decision tree induction – optimising decision classifications using GAs – scheduling tasks with intertask communication onto multiprocessors by GAs – design of robust networks with GA – effect of degenerate coding on GAs – multiple traffic signal control using a GA – evolving musical harmonisation – niched-penalty approach for constraint handling in GAs – GA with dynamic population size – GA with dynamic niche clustering for multimodal function optimisation Soft computing and uncertainty: self-adaptation of evolutionary constructed decision trees by information spreading – evolutionary programming of near optimal NNs

Keywords

algorithms classification genetic algorithms image processing learning modeling optimization proving sketch uncertainty

Authors and affiliations

  • Andrej Dobnikar
    • 1
  • Nigel C. Steele
    • 2
  • David W. Pearson
    • 3
  • Rudolf F. Albrecht
    • 4
  1. 1.Fakulteta za Računalništvo in InformatikoUniverza v LjubljaniLjubljanaSlovenia
  2. 2.Division of Mathematics, School of Mathematical and Information SciencesCoventry UniversityCoventryUK
  3. 3.Laboratoire de Génie Informatique et Ingénierie de ProductionEcole pour les Etudes et la Recherche en Informatique et ElectroniqueNimesFrance
  4. 4.Institut für InformatikUniversität InnsbruckInnsbruckAustria

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-7091-6384-9
  • Copyright Information Springer-Verlag/Wien 1999
  • Publisher Name Springer, Vienna
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-211-83364-3
  • Online ISBN 978-3-7091-6384-9
  • About this book