Title
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence, 33),Used
Delivery time: 8-12 business days (International)
Im 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 youre 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 computations 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 datamining perspectiveimplicitinEDAsopensuptheworldofoptimizationtonewme ods of dataguided adaptation that can further speed solutions through the construction and utilization of e?ective surrogates, hybrids, and parallel and temporal decompositions.
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This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).
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