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Artificial Neural Nets and Genetic Algorithms

Proceedings of the International Conference in Norwich, U.K., 1997

  • Conference proceedings
  • © 1998

Overview

Authors:
  1. George D. Smith

    1. School of Information Systems, University of East Anglia, Norwich, UK

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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 (137 papers)

  1. Front Matter

    Pages i-xvi
    Download chapter PDF

  2. Robotics and Sensors

    1. Obstacle Identification by an Ultrasound Sensor Using Neural Networks

      • D. Diep, A. Johannet, P. Bonnefoy, F. Harroy
      Pages 1-5

    2. A Modular Reinforcement Learning Architecture for Mobile Robot Control

      • R. M. Rylatt, C. A. Czarnecki, T. W. Routen
      Pages 6-10

    3. Timing without Time — An Experiment in Evolutionary Robotics

      • H. H. Lund
      Pages 11-15

    4. Incremental Acquisition of Complex Behaviour by Structured Evolution

      • S. Perkins, G. Hayes
      Pages 16-20

    5. Evolving Neural Controllers for Robot Manipulators

      • R. Salama, R. Owens
      Pages 21-25

    6. Using Genetic Algorithms with Variable-length Individuals for Planning Two-Manipulators Motion

      • J. Riquelme, M. A. Ridao, E. F. Camacho, M. Toro
      Pages 26-30

  4. Power Systems

    1. Electric Load Forecasting with Genetic Neural Networks

      • F. J. Marín, F. Sandoval
      Pages 49-52

    2. Multiobjective Pressurised Water Reactor Reload Core Design using a Genetic Algorithm

      • G. T. Parks
      Pages 53-57

    3. Using Artificial Neural Networks to Model Non-Linearity in a Complex System

      • P. Weller, A. Thompson, R. Summers
      Pages 58-61

    4. Transit Time Estimation by Artificial Neural Networks

      • T. Tambouratzis, M. Antonopoulos-Domis, M. Marseguerra, E. Padovani
      Pages 62-65

  5. Evolware

    1. Evolving Asynchronous and Scalable Non-uniform Cellular Automata

      • M. Sipper, M. Tomassini, M. S. Capcarrere
      Pages 66-70

    2. One-Chip Evolvable Hardware: 1C-EHW

      • H. de Garis
      Pages 71-77

  6. Vision

    1. Evolving Low-Level Vision Capabilities with the GENCODER Genetic Programming Environment

      • P. Ziemeck, H. Ritter
      Pages 78-82

    2. NLRFLA: A Supervised Learning Algorithm for the Development of Non-Linear Receptive Fields

      • S. L. Funk, I. Kumazawa, J. M. Kennedy
      Pages 83-87

    3. Fuzzy-tuned Stochastic Scanpaths for AGV Vision

      • I. J. Griffiths, Q. H. Mehdi, N. E. Gough
      Pages 88-92
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Keywords

About this book

This is the third in a series of conferences devoted primarily to the theory and applications of artificial neural networks and genetic algorithms. The first such event was held in Innsbruck, Austria, in April 1993, the second in Ales, France, in April 1995. We are pleased to host the 1997 event in the mediaeval city of Norwich, England, and to carryon the fine tradition set by its predecessors of providing a relaxed and stimulating environment for both established and emerging researchers working in these and other, related fields. This series of conferences is unique in recognising the relation between the two main themes of artificial neural networks and genetic algorithms, each having its origin in a natural process fundamental to life on earth, and each now well established as a paradigm fundamental to continuing technological development through the solution of complex, industrial, commercial and financial problems. This is well illustrated in this volume by the numerous applications of both paradigms to new and challenging problems. The third key theme of the series, therefore, is the integration of both technologies, either through the use of the genetic algorithm to construct the most effective network architecture for the problem in hand, or, more recently, the use of neural networks as approximate fitness functions for a genetic algorithm searching for good solutions in an 'incomplete' solution space, i.e. one for which the fitness is not easily established for every possible solution instance.

Authors and Affiliations

  • School of Information Systems, University of East Anglia, Norwich, UK

    George D. Smith

  • 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

Bibliographic Information

  • Book Title: Artificial Neural Nets and Genetic Algorithms

  • Book Subtitle: Proceedings of the International Conference in Norwich, U.K., 1997

  • Authors: George D. Smith, Nigel C. Steele, Rudolf F. Albrecht

  • DOI: https://doi.org/10.1007/978-3-7091-6492-1

  • Publisher: Springer Vienna

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag Wien 1998

  • Softcover ISBN: 978-3-211-83087-1Published: 22 June 1998

  • eBook ISBN: 978-3-7091-6492-1Published: 06 December 2012

  • Edition Number: 1

  • Number of Pages: XVI, 634

  • Topics: Artificial Intelligence, Complexity, Health Informatics, Operations Research/Decision Theory

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