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: Parallel Genetic Algorithms for Financial Pattern Discovery Using GPUs
  name:
    name part: Baúto, João.
    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: Neves, Rui.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Horta, Nuno.
    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: XIV, 91 p. 50 illus. online resource.
  abstract: This Brief presents a study of SAX/GA, an algorithm to optimize market trading strategies, to understand how the sequential implementation of SAX/GA and genetic operators work to optimize possible solutions. This study is later used as the baseline for the development of parallel techniques capable of exploring the identified points of parallelism that simply focus on accelerating the heavy duty fitness function to a full GPU accelerated GA. .
  table of contents: Introduction -- State-of-the-Art in Pattern Recognition Techniques -- SAX/GA CPU Approach -- GPU-accelerated SAX/GA -- Conclusions and Future Work in the Field.
  note: by João Baúto, Rui Neves, Nuno Horta.
  subject:
    topic: Computational intelligence
  subject:
    topic: Financial engineering
  subject:
    topic: Economics, Mathematical
  subject:
    topic: Computational Intelligence
  subject:
    topic: Financial Engineering
  subject:
    topic: Quantitative Finance
  classification: Q342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: SpringerBriefs in Computational Intelligence
  identifier: 9783319733296
  identifier: https://doi.org/10.1007/978-3-319-73329-6
  record info:
    record creation date: 180204
    record change date: 20191022131251.0
    record identifier: 978-3-319-73329-6