- Series: Operations Research/Computer Science Interfaces Series (Book 45)
- Hardcover: 196 pages
- Publisher: Springer; 1st Edition. 2nd Printing. 2008 edition (November 6, 2008)
- Language: English
- ISBN-10: 038709623X
- ISBN-13: 978-0387096230
- Product Dimensions: 6.1 x 0.5 x 9.2 inches
- Shipping Weight: 14.9 ounces (View shipping rates and policies)
- Average Customer Review: Be the first to review this item
- Amazon Best Sellers Rank: #13,901,248 in Books (See Top 100 in Books)
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Reactive Search and Intelligent Optimization (Operations Research/Computer Science Interfaces Series) Hardcover – November 6, 2008
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From the Back Cover
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.
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