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: Multimodal Optimization by Means of Evolutionary Algorithms
  name:
    name part: Preuss, Mike.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2015
    edition: 1st ed. 2015.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XX, 189 p. 42 illus., 5 illus. in color. online resource.
  abstract: This book offers the first comprehensive taxonomy for multimodal optimization algorithms, work with its root in topics such as niching, parallel evolutionary algorithms, and global optimization. The author explains niching in evolutionary algorithms and its benefits; he examines their suitability for use as diagnostic tools for experimental analysis, especially for detecting problem (type) properties; and he measures and compares the performances of niching and canonical EAs using different benchmark test problem sets. His work consolidates the recent successes in this domain, presenting and explaining use cases, algorithms, and performance measures, with a focus throughout on the goals of the optimization processes and a deep understanding of the algorithms used. The book will be useful for researchers and practitioners in the area of computational intelligence, particularly those engaged with heuristic search, multimodal optimization, evolutionary computing, and experimental analysis.
  table of contents: Introduction: Towards Multimodal Optimization -- Experimentation in Evolutionary Computation -- Groundwork for Niching -- Nearest-Better Clustering -- Niching Methods and Multimodal Optimization Performance -- Nearest-Better Based Niching.
  note: by Mike Preuss.
  subject:
    topic: Computer science
  subject:
    topic: Algorithms
  subject:
    topic: Mathematical optimization
  subject:
    topic: Computational intelligence
  subject:
    topic: Computer Science
  subject:
    topic: Algorithm Analysis and Problem Complexity
  subject:
    topic: Computational Intelligence
  subject:
    topic: Optimization
  classification: QA76.9.A43
  classification: 005.1
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Natural Computing Series
  identifier: 9783319074078
  identifier: http://dx.doi.org/10.1007/978-3-319-07407-8
  record info:
    record creation date: 151127
    record change date: 20151128192421.0
    record identifier: 978-3-319-07407-8