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  • Book
  • © 2009

Reactive Search and Intelligent Optimization

  • Comprehensive introduction to principles of reactive search
  • Focuses on methods, but uses problems to help demonstrate points
  • Important reading for anyone in decision science within areas of business, engineering, economics, or science

Part of the book series: Operations Research/Computer Science Interfaces Series (ORCS, volume 45)

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Table of contents (13 chapters)

  1. Front Matter

    Pages 1-9
  2. Introduction: Machine Learning for Intelligent Optimization

    • Roberto Battiti, Franco Mascia, Mauro Brunato
    Pages 1-8
  3. Reacting on the neighborhood

    • Roberto Battiti, Franco Mascia, Mauro Brunato
    Pages 1-15
  4. Reacting on the Annealing Schedule

    • Roberto Battiti, Franco Mascia, Mauro Brunato
    Pages 1-9
  5. Reactive Prohibitions

    • Roberto Battiti, Franco Mascia, Mauro Brunato
    Pages 1-24
  6. Reacting on the Objective Function

    • Roberto Battiti, Franco Mascia, Mauro Brunato
    Pages 1-9
  7. Reacting on the Objective Function

    • Roberto Battiti, Franco Mascia, Mauro Brunato
    Pages 1-13
  8. Supervised Learning

    • Roberto Battiti, Franco Mascia, Mauro Brunato
    Pages 1-33
  9. Reinforcement Learning

    • Roberto Battiti, Franco Mascia, Mauro Brunato
    Pages 1-12
  10. Algorithm Portfolios and Restart Strategies

    • Roberto Battiti, Franco Mascia, Mauro Brunato
    Pages 1-11
  11. Racing

    • Roberto Battiti, Franco Mascia, Mauro Brunato
    Pages 1-9
  12. Teams of Interacting Solvers

    • Roberto Battiti, Franco Mascia, Mauro Brunato
    Pages 1-12
  13. Metrics, Landscapes and Features

    • Roberto Battiti, Franco Mascia, Mauro Brunato
    Pages 1-14
  14. Open Problems

    • Roberto Battiti, Franco Mascia, Mauro Brunato
    Pages 1-3
  15. Back Matter

    Pages 1-16

About this book

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 forthe automated tuning of these parameters.

Authors and Affiliations

  • Dipto. Informatica e Telecomunicazioni, Universitá Trento, Trento, Italy

    Roberto Battiti, Mauro Brunato, Franco Mascia

Bibliographic Information

Buy it now

Buying options

eBook USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 159.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access