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Book synopsis

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.

Book details

Book category Books in English Computing & information technology Computer science

• Full title: Scalable Optimization via Probabilistic Modeling
• Subtitle: From Algorithms to Applications
• Author: Martin Pelikan, Kumara Sastry, Erick Cantú-Paz
• Language: English
• Binding: Hardback
• Number of pages: 349
• EAN: 9783540349532
• ISBN: 3540349537
• ID: 02718189
• Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
• Weight: 1540 g
• Dimensions: 297 × 210 × 27 mm
• Date of publishing: 25. September 2006

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