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: Black box optimization, machine learning, and no-free lunch theorems
  name:
    name part: Pardalos, P. M. (Panos M.)
    name part: 1954-
    role:
      role term: editor.
  name:
    name part: Rasskazova, Varvara
    role:
      role term: editor.
  name:
    name part: Vrahatis, Michael N.
    name part: 1955-
    role:
      role term: editor.
  name:
    name part: ProQuest (Firm)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2021
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  table of contents: Learning enabled constrained black box optimization (Archetti) -- Black-box optimization: Methods and applications (Hasan) -- Tuning algorithms for stochastic black-box optimization: State of the art and future perspectives (Bartz-Beielstein) -- Quality diversity optimization: A novel branch of stochastic optimization (Chatzilygeroudis) -- Multi-objective evolutionary algorithms: Past, present and future (Coello C.A) -- Black-box and data driven computation (Du) -- Mathematically rigorous global optimization and fuzzy optimization: A brief comparison of paradigms, methods, similarities and differences (Kearfott) -- Optimization under Uncertainty Explains Empirical Success of Deep Learning Heuristics (Kreinovich) -- Variable neighborhood programming as a tool of machine learning (Mladenovic) -- Non-lattice covering and quanitization of high dimensional sets (Zhigljavsky) -- Finding effective SAT partitionings via black-box optimization (Semenov) -- The No Free Lunch Theorem: What are its main implications for the optimization practice? ( Serafino) -- What is important about the No Free Lunch theorems? (Wolpert).
  note: Panos M. Pardalos, Varvara Rasskazova, Michael N. Vrahatis, editors.
  note: Electronic resource.
  subject:
    topic: Machine learning
    topic: Mathematics
  subject:
    topic: Mathematical optimization
  subject:
    topic: Computer algorithms
  classification: Q325.5 .B53 2021
  classification: 006.3/10151
  related item:
    title info:
      title: Springer optimization and its applications ; v. 170
  identifier: 9783030665159
  identifier: 3030665151
  identifier: 3030665143
  identifier: 9783030665142
  identifier: https://ebookcentral.proquest.com/lib/tamucs/detail.action
  record info:
    record content source: NhCcYBP
    record creation date: 210601
    record change date: 20250816223253.6
    record identifier: in00004365125