Scalable Optimization via Probabilistic Modeling
(Sprache: Englisch)
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...
Leider schon ausverkauft
versandkostenfrei
Buch
Fr. 288.90
inkl. MwSt.
- Kreditkarte, Paypal, Rechnungskauf
- 30 Tage Widerrufsrecht
Produktdetails
Produktinformationen zu „Scalable Optimization via Probabilistic Modeling “
Klappentext zu „Scalable Optimization via Probabilistic Modeling “
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.
Inhaltsverzeichnis zu „Scalable Optimization via Probabilistic Modeling “
- The Factorized Distribution Algorithm and the Minimum Relative Entropy Principle- Linkage Learning via Probabilistic Modeling in the ECGA
- Hierarchical Bayesian Optimization Algorithm
- Numerical Optimization with Real-Valued Estimation-of- Distribution Algorithms
- A Survey of Probabilistic Model Building Genetic Programming
- Efficiency Enhancement of Estimation of Distribution Algorithms
- Design of Parallel Estimation of Distribution Algorithms
- Incorporating a priori Knowledge in Probabilistic-Model Based Optimization
- Multiobjective Estimation of Distribution Algorithms
- Effective and Reliable Online Classification Combining XCS with EDA Mechanisms
- Military Antenna Design Using a Simple Genetic Algorithm and hBOA
- Feature Subset Selection with Hybrids of Filters and Evolutionary Algorithms
- BOA for Nurse Scheduling
- Searching for Ground States of Ising Spin Glasses with Hierarchical BOA and Cluster Exact Approximation
Bibliographische Angaben
- 2006, 349 Seiten, 98 Schwarz-Weiss-Abbildungen, Masse: 16,2 x 24,6 cm, Gebunden, Englisch
- Herausgegeben: Martin Pelikan, Kumara Sastry, Erick Cantu-Paz
- Verlag: Springer
- ISBN-10: 3540349537
- ISBN-13: 9783540349532
- Erscheinungsdatum: 25.09.2006
Sprache:
Englisch
Kommentar zu "Scalable Optimization via Probabilistic Modeling"
0 Gebrauchte Artikel zu „Scalable Optimization via Probabilistic Modeling“
Zustand | Preis | Porto | Zahlung | Verkäufer | Rating |
---|
Schreiben Sie einen Kommentar zu "Scalable Optimization via Probabilistic Modeling".
Kommentar verfassen