Artificial Neural Nets and Genetic Algorithms

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

  • George D. Smith
  • Nigel C. Steele
  • Rudolf F. Albrecht

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Robotics and Sensors

  3. ANN Architectures

  4. Power Systems

    1. F. J. Marín, F. Sandoval
      Pages 49-52
    2. T. Tambouratzis, M. Antonopoulos-Domis, M. Marseguerra, E. Padovani
      Pages 62-65
  5. Evolware

    1. M. Sipper, M. Tomassini, M. S. Capcarrere
      Pages 66-70
    2. H. de Garis
      Pages 71-77
  6. Vision

About these proceedings

Introduction

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.

Keywords

algorithms artificial neural network cognition genetic algorithms genetic programming heuristics image processing learning modeling neural networks optimization reinforcement learning robot robotics scheduling

Authors and affiliations

  • George D. Smith
    • 1
  • Nigel C. Steele
    • 2
  • Rudolf F. Albrecht
    • 3
  1. 1.School of Information SystemsUniversity of East AngliaNorwichUK
  2. 2.Division of Mathematics, School of Mathematical and Information SciencesCoventry UniversityCoventryUK
  3. 3.Institut für InformatikUniversität InnsbruckInnsbruckAustria

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-7091-6492-1
  • Copyright Information Springer-Verlag/Wien 1998
  • Publisher Name Springer, Vienna
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-211-83087-1
  • Online ISBN 978-3-7091-6492-1
  • About this book
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