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: Foraging-Inspired Optimisation Algorithms
  name:
    name part: Brabazon, Anthony.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: McGarraghy, Seán.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2018
    edition: 1st ed. 2018.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVIII, 478 p. online resource.
  abstract: This book is an introduction to relevant aspects of the foraging literature for algorithmic design, and an overview of key families of optimization algorithms that stem from a foraging metaphor. The authors first offer perspectives on foraging and foraging-inspired algorithms for optimization, they then explain the techniques inspired by the behaviors of vertebrates, invertebrates, and non-neuronal organisms, and they then discuss algorithms based on formal models of foraging, how to evolve a foraging strategy, and likely future developments. No prior knowledge of natural computing is assumed. This book will be of particular interest to graduate students, academics and practitioners in computer science, informatics, data science, management science, and other application domains.
  table of contents: Introduction -- Formal Models of Foraging -- Sensor Modalities -- Individual and Social Learning -- Introduction to Foraging Algorithms -- Mammals -- Bird Foraging Algorithms -- Fish Algorithms -- Ant Foraging Algorithms -- Honeybee Inspired Algorithms -- Bioluminescence Algorithms -- Spider Algorithms -- Worm Algorithm -- Bacteria Inspired Algorithms -- Slime Mould Foraging -- Plant Foraging Algorithms -- Group Search and Predatory Search -- Evolving Foraging Algorithms -- Conclusions.
  note: by Anthony Brabazon, Seán McGarraghy.
  subject:
    topic: Computers
  subject:
    topic: Computational intelligence
  subject:
    topic: Artificial intelligence
  subject:
    topic: Operations research
  subject:
    topic: Management science
  subject:
    topic: Decision making
  subject:
    topic: Theory of Computation
  subject:
    topic: Computational Intelligence
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Operations Research, Management Science
  subject:
    topic: Operations Research/Decision Theory
  classification: QA75.5-76.95
  classification: QA76.63
  classification: 004.0151
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Natural Computing Series
  identifier: 9783319591568
  identifier: https://doi.org/10.1007/978-3-319-59156-8
  record info:
    record creation date: 180926
    record change date: 20191022092916.0
    record identifier: 978-3-319-59156-8