Scalable optimization via probabilistic modeling : from algorithms to applications
Author(s)
Bibliographic Information
Scalable optimization via probabilistic modeling : from algorithms to applications
（Studies in computational intelligence, v. 33）
Springer, c2006
Available at / 5 libraries
-
No Libraries matched.
- Remove all filters.
Note
Includes bibliographical references
Description and Table of Contents
Description
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.
Table of Contents
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.
by "Nielsen BookData"