Pelikan / Sastry / Cantú-Paz Scalable Optimization via Probabilistic Modeling

From Algorithms to Applications
Erscheinungsjahr 2007
ISBN: 978-3-540-34954-9
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 33, 349 Seiten, eBook

Reihe: Studies in Computational Intelligence

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.

Zielgruppe

Research

Autoren/Hrsg.

Pelikan, Martin

Sastry, Kumara

Cantú-Paz, Erick

Inhaltsverzeichnis

The Factorized Distribution Algorithm and the Minimum Relative Entropy Principle.- Linkage Learning via Probabilistic Modeling in the Extended Compact Genetic Algorithm (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.