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:
    non sort: An
    title: introduction to genetic algorithms
  name:
    name part: Mitchell, Melanie
    name part: (Computer scientist)
    role:
      role term: creator
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: mau
    place:
      place term: Cambridge, Mass
    publisher: MIT Press
    date issued: ©1996
    date issued: 1996
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (viii, 205 pages) : illustrations.
  abstract: Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics--particularly in machine learning, scientific modeling, and artificial life--and reviews a broad span of research, including the work of Mitchell and her colleagues.The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines.An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.
  note: Melanie Mitchell.
  note: "A Bradford book."
  subject:
    topic: Genetic algorithms
  subject:
    topic: Genetics
    topic: Computer simulation
  subject:
    topic: Genetics
    topic: Mathematical models
  subject:
    topic: Genetics
    topic: Mathematical models
  subject:
    topic: COMPUTER SCIENCE/General
  classification: QH441.2 .M55 1996eb
  classification: 575.1/01/13
  identifier: 9780262280013
  identifier: 0262280019
  identifier: 0585030944
  identifier: 9780585030944
  identifier: 9780262133166
  identifier: 0262133164
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: OCoLC-P
    record creation date: 950623
    record change date: 20221121162447.2
    record identifier: in00004601340
    language of cataloging:
      language term: eng