Operations Research/Computer Science Interfaces Series

Reactive Search and Intelligent Optimization

Authors: Battiti, Roberto, Brunato, Mauro, Mascia, Franco

  • Presents the main principles of reactive search and intelligent optimization and clearly shows how they can be used in problem solving

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eBook $119.00
price for USA (gross)
  • ISBN 978-0-387-09624-7
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $159.00
price for USA
  • ISBN 978-0-387-09623-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $159.00
price for USA
  • ISBN 978-1-4419-3499-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

Reactive Search integrates sub-symbolic machine learning techniques into search heuristics for solving complex optimization problems.  By automatically adjusting the working parameters, a reactive search self-tunes and adapts, effectively learning by doing until a solution is found.  Intelligent Optimization, a superset of Reactive Search, concerns online and off-line schemes based on the use of memory, adaptation, incremental development of models, experimental algorithms applied to optimization, intelligent tuning and design of heuristics.

 

Reactive Search and Intelligent Optimization is an excellent introduction to the main principles of reactive search, as well as an attempt to develop some fresh intuition for the approaches. The book looks at different optimization possibilities with an emphasis on opportunities for learning and self-tuning strategies.  While focusing more on methods than on problems, problems are introduced wherever they help make the discussion more concrete, or when a specific problem has been widely studied by reactive search and intelligent optimization heuristics.

 

Individual chapters cover reacting on the neighborhood; reacting on the annealing schedule; reactive prohibitions; model-based search; reacting on the objective function; relationships between reactive search and reinforcement learning; and much more.  Each chapter is structured to show basic issues and algorithms; the parameters critical for the success of the different methods discussed; and opportunities and schemes for the automated tuning of these parameters.  Anyone working in decision making in business, engineering, economics or science will find a wealth of information here.

 

Table of contents (13 chapters)

  • Introduction: Machine Learning for Intelligent Optimization

    Battiti, Roberto (et al.)

    Pages 1-8

  • Reacting on the neighborhood

    Battiti, Roberto (et al.)

    Pages 1-15

  • Reacting on the Objective Function

    Battiti, Roberto (et al.)

    Pages 1-13

  • Open Problems

    Battiti, Roberto (et al.)

    Pages 1-3

  • Supervised Learning

    Battiti, Roberto (et al.)

    Pages 1-33

Buy this book

eBook $119.00
price for USA (gross)
  • ISBN 978-0-387-09624-7
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $159.00
price for USA
  • ISBN 978-0-387-09623-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $159.00
price for USA
  • ISBN 978-1-4419-3499-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Reactive Search and Intelligent Optimization
Authors
Series Title
Operations Research/Computer Science Interfaces Series
Series Volume
45
Copyright
2009
Publisher
Springer US
Copyright Holder
Springer-Verlag US
eBook ISBN
978-0-387-09624-7
DOI
10.1007/978-0-387-09624-7
Hardcover ISBN
978-0-387-09623-0
Softcover ISBN
978-1-4419-3499-4
Series ISSN
1387-666X
Edition Number
1
Number of Pages
X, 196
Number of Illustrations and Tables
74 b/w illustrations
Topics