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: Principles in Noisy Optimization
    sub title: Applied to Multi-agent Coordination
  name:
    name part: Rakshit, Pratyusha.
    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: Konar, Amit.
    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: si
    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: XVI, 367 p. 88 illus., 54 illus. in color. online resource.
  abstract: Noisy optimization is a topic of growing interest for researchers working on mainstream optimization problems. Although several techniques for dealing with stochastic noise in optimization problems are covered in journals and conference proceedings, today there are virtually no books that approach noisy optimization from a layman's perspective; this book remedies that gap. Beginning with the foundations of evolutionary optimization, the book subsequently explores the principles of noisy optimization in single and multi-objective settings, and presents detailed illustrations of the principles developed for application in real-world multi-agent coordination problems. Special emphasis is given to the design of intelligent algorithms for noisy optimization in real-time applications. The book is unique in terms of its content, writing style and above all its simplicity, which will appeal to readers with a broad range of backgrounds. The book is divided into 7 chapters, the first of which provides an introduction to Swarm and Evolutionary Optimization algorithms. Chapter 2 includes a thorough review of agent architectures for multi-agent coordination. In turn, Chapter 3 provides an extensive review of noisy optimization, while Chapter 4 addresses issues of noise handling in the context of single-objective optimization problems. An illustrative case study on multi-robot path-planning in the presence of measurement noise is also highlighted in this chapter. Chapter 5 deals with noisy multi-objective optimization and includes a case study on noisy multi-robot box-pushing. In Chapter 6, the authors examine the scope of various algorithms in noisy optimization problems. Lastly, Chapter 7 summarizes the main results obtained in the previous chapters and elaborates on the book's potential with regard to real-world noisy optimization problems.
  table of contents: Chapter 1. Foundations in Evolutionary Optimization Algorithms -- Chapter 2. Multi-agent Coordination -- Chapter 3. Evolutionary Algorithms in Presence of Noise -- Chapter 4. Learning based Noisy Optimization -- Chapter 5. Noisy Coordination in Multi-objective Settings -- Chapter 6. Integrating Principles of Noisy Optimization with Evolutionary Optimization -- Chapter 7. Conclusion and Future Direction.
  note: by Pratyusha Rakshit, Amit Konar.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Mathematical optimization
  subject:
    topic: Computers
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Optimization
  subject:
    topic: Theory of Computation
  classification: Q334-342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Cognitive Intelligence and Robotics
  identifier: 9789811086427
  identifier: https://doi.org/10.1007/978-981-10-8642-7
  record info:
    record creation date: 181119
    record change date: 20191028122527.0
    record identifier: 978-981-10-8642-7