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: Evolutionary multi-objective system design
    sub title: theory and applications
  name:
    name part: Nedjah, Nadia
    role:
      role term: editor.
  name:
    name part: Macedo Mourelle, Luiza de
    role:
      role term: editor.
  name:
    name part: Lopes, Heitor Silvério
    role:
      role term: editor.
  name:
    name part: Taylor & Francis
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: flu
    date issued: 2018
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xxiii, 217 pages) : illustrations
  abstract: Real-world engineering problems often require concurrent optimization of several design objectives, which are conflicting in cases. This type of optimization is generally called multi-objective or multi-criterion optimization. The area of research that applies evolutionary methodologies to multi-objective optimization is of special and growing interest. It brings a viable computational solution to many real-world problems. Generally, multi-objective engineering problems do not have a straightforward optimal design. These kinds of problems usually inspire several solutions of equal efficiency, which achieve different trade-offs. Decision makers' preferences are normally used to select the most adequate design. Such preferences may be dictated before or after the optimization takes place. They may also be introduced interactively at different levels of the optimization process. Multi-objective optimization methods can be subdivided into classical and evolutionary. The classical methods usually aim at a single solution while the evolutionary methods provide a whole set of so-called Pareto-optimal solutions. Evolutionary Multi-Objective System Design: Theory and Applications provides a representation of the state-of-the-art in evolutionary multi-objective optimization research area and related new trends. It reports many innovative designs yielded by the application of such optimization methods. It also presents the application of multi-objective optimization to the following problems: Embrittlement of stainless steel coated electrodes Learning fuzzy rules from imbalanced datasets Combining multi-objective evolutionary algorithms with collective intelligence Fuzzy gain scheduling control Smart placement of roadside units in vehicular networks Combining multi-objective evolutionary algorithms with quasi-simplex local search Design of robust substitution boxes Protein structure prediction problem Core assignment for efficient network-on-chip-based system design
  note: edited by Nadia Nedjah, Luiza De Macedo Mourelle, Heitor Silverio Lopes.
  note: Includes bibliographical references and index.
  note: Nadia Nedjah, Luiza De Macedo Mourelle, Heitor Silverio Lopes
  subject:
    topic: Computational intelligence
  subject:
    topic: Evolutionary computation
  subject:
    topic: Mathematical optimization
  subject:
    topic: Artificial intelligence
  subject:
    topic: Machine learning
  subject:
    topic: Data mining
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Data Mining
  subject:
    topic: Machine Learning
  subject:
    topic: Intelligence informatique
  subject:
    topic: Réseaux neuronaux à structure évolutive
  subject:
    topic: Optimisation mathématique
  subject:
    topic: Intelligence artificielle
  subject:
    topic: Apprentissage automatique
  subject:
    topic: Exploration de données (Informatique)
  subject:
    topic: artificial intelligence
  subject:
    topic: COMPUTERS
    topic: Computer Graphics
    topic: Game Programming & Design
  subject:
    topic: COMPUTERS
    topic: Machine Theory
  subject:
    topic: COMPUTERS
    topic: Computer Engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computational intelligence
  subject:
    topic: Data mining
  subject:
    topic: Evolutionary computation
  subject:
    topic: Machine learning
  subject:
    topic: Mathematical optimization
  classification: QA76.618 .E96 2018eb
  classification: 006.3823
  related item:
    title info:
      title: Evolutionary multi-objective system design
    origin info:
      publisher: Boca Raton : Chapman & Hall/CRC, 2018
    identifier: (OCoLC)1031872941
  related item:
    title info:
      title: Chapman & Hall/CRC computer and information science series
  identifier: 9781315366845
  identifier: 1315366843
  identifier: 9781315349596
  identifier: 1315349590
  identifier: 9781498780292
  identifier: 1498780296
  identifier: 9781315330563
  identifier: 1315330563
  identifier: 1498780288
  identifier: 9781498780285
  identifier: 9781315366845 Taylor & Francis
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: TYFRS
    record creation date: 200721
    record change date: 20241108170119.1
    record identifier: in00005526321
    language of cataloging:
      language term: eng