rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Artificial Neural Nets and Genetic Algorithms
    sub title: Proceedings of the International Conference in Innsbruck, Austria, 1993
  name:
    name part: Albrecht, Rudolf F.
    role:
      role term: creator
  name:
    name part: Reeves, Colin R.
  name:
    name part: Steele, N. C. (Nigel C.)
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: au
    date issued: 1993
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (xiii, 737 pages 503 illustrations)
  abstract: 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 the subjects of contributions to this volume. There are contributions reporting theoretical developments in the design of neural networks, and in the management of their learning. In a number of contributions, applications to speech recognition tasks, control of industrial processes as well as to credit scoring, and so on, are reflected. Regarding genetic algorithms, several methodological papers consider how genetic algorithms can be improved using an experimental approach, as well as by hybridizing with other useful techniques such as tabu search. The closely related area of classifier systems also receives a significant amount of coverage, aiming at better ways for their implementation. Further, while there are many contributions which explore ways in which genetic algorithms can be applied to real problems, nearly all involve some understanding of the context in order to apply the genetic algorithm paradigm more successfully. That this can indeed be done is evidenced by the range of applications covered in this volume.
  note: edited by Rudolf F. Albrecht, Colin R. Reeves, Nigel C. Steele.
  note: Electronic resource.
  subject:
    topic: Computer science
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computer vision
  subject:
    topic: Optical pattern recognition
  subject:
    topic: Algorithms
  subject:
    topic: Distribution (Probability theory)
  subject:
    topic: Statistics
  subject:
    topic: Algorithms
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computer science
  subject:
    topic: Computer vision
  subject:
    topic: Distribution (Probability theory)
  subject:
    topic: Optical pattern recognition
  subject:
    topic: Statistics
  classification: Q334-342
  classification: TJ210.2-211.495
  classification: 006.3
  identifier: 9783709175330 (electronic bk.)
  identifier: 370917533X (electronic bk.)
  identifier: 9783211824597
  identifier: 3211824596
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: AU@
    record creation date: 121227
    record change date: 20260422222313.7
    record identifier: in00003565200
    language of cataloging:
      language term: eng