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91454801
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence, 33)
Item #:
91454801
INR 18021
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What Stands Out
Scalable Optimization
This book offers innovative approaches to scalable optimization techniques, allowing practitioners to efficiently tackle complex problems in various fields, enhancing both algorithm performance and application effectiveness.
Probabilistic Modeling
It integrates probabilistic modeling into optimization, equipping readers with advanced methodologies that improve decision-making under uncertainty, making it invaluable for researchers and professionals in computational intelligence.
Diverse Applications
The text covers a wide range of applications, demonstrating practical implementations of discussed algorithms, thereby bridging theory and real-world use, which appeals to academics and industry experts alike.
Product Details
- I’m not usually a fan of edited volumes. Too often they are an incoherent hodgepodge of remnants, renegades, or rejects foisted upon an unsuspecting reading public under a misleading or fraudulent title. The volume Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications is a worthy addition to your library because it succeeds on exactly those dimensions where so many edited volumes fail. For example, take the title, Scalable Optimization via Probabilistic M- eling: From Algorithms to Applications. You need not worry that you’re going to pick up this book and ?nd stray articles about anything else. This book focuseslikealaserbeamononeofthehottesttopicsinevolutionary compu- tion over the last decade or so: estimation of distribution algorithms (EDAs). EDAs borrow evolutionary computation’s population orientation and sel- tionism and throw out the genetics to give us a hybrid of substantial power, elegance, and extensibility. The article sequencing in most edited volumes is hard to understand, but from the get go the editors of this volume have assembled a set of articles sequenced in a logical fashion. The book moves from design to e?ciency enhancement and then concludes with relevant applications. The emphasis on e?ciency enhancement is particularly important, because the data-mining perspectiveimplicitinEDAsopensuptheworldofoptimizationtonewme- ods of data-guided adaptation that can further speed solutions through the construction and utilization of e?ective surrogates, hybrids, and parallel and temporal decompositions.
| Publisher | Springer |
| Publication date | November 30, 2010 |
| Edition | 1st |
| Language | English |
| Print length | 369 pages |
| ISBN-10 | 3642071163 |
| ISBN-13 | 978-3642071164 |
| Item Weight | 1.15 pounds (520 grams) |
| Dimensions | 6.1 x 0.84 x 9.25 inches (15.5 x 2.1 x 23.5 cm) |
| Part of series | Studies in Computational Intelligence |
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