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 find stray articles about anything else. This book focuseslikealaserbeamononeof thehottesttopicsinevolutionary 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 efficiency enhancement and then concludes with relevant applications. The emphasis on efficiency enhancement is particularly important, because the data-mining perspective implicit in EDAs opens up the world of optimization to new me- ods of data-guided adaptation that can further speed solutions through the construction and utilization of effective surrogates, hybrids, and parallel and temporal decompositions.
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Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications
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 find stray articles about anything else. This book focuseslikealaserbeamononeof thehottesttopicsinevolutionary 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 efficiency enhancement and then concludes with relevant applications. The emphasis on efficiency enhancement is particularly important, because the data-mining perspective implicit in EDAs opens up the world of optimization to new me- ods of data-guided adaptation that can further speed solutions through the construction and utilization of effective surrogates, hybrids, and parallel and temporal decompositions.
169.99
In Stock
5
1
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications
349
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications
349Paperback(Softcover reprint of hardcover 1st ed. 2006)
$169.99
169.99
In Stock
Product Details
| ISBN-13: | 9783642071164 |
|---|---|
| Publisher: | Springer Berlin Heidelberg |
| Publication date: | 11/25/2010 |
| Series: | Studies in Computational Intelligence , #33 |
| Edition description: | Softcover reprint of hardcover 1st ed. 2006 |
| Pages: | 349 |
| Product dimensions: | 6.10(w) x 9.25(h) x 0.03(d) |
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