Hierarchical Bayesian Optimization Algorithm - Toward a New Generation of Evolutionary Algorithms (Hardcover, 2005 ed.)


This book provides a framework for the design of competent optimization techniques by combining advanced evolutionary algorithms with state-of-the-art machine learning techniques. The primary focus of the book is on two algorithms that replace traditional variation operators of evolutionary algorithms, by learning and sampling Bayesian networks: the Bayesian optimization algorithm (BOA) and the hierarchical BOA (hBOA). They provide a scalable solution to a broad class of problems. The book provides an overview of evolutionary algorithms that use probabilistic models to guide their search, motivates and describes BOA and hBOA in a way accessible to a wide audience, and presents numerous results confirming that they are revolutionary approaches to black-box optimization.


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Product Description

This book provides a framework for the design of competent optimization techniques by combining advanced evolutionary algorithms with state-of-the-art machine learning techniques. The primary focus of the book is on two algorithms that replace traditional variation operators of evolutionary algorithms, by learning and sampling Bayesian networks: the Bayesian optimization algorithm (BOA) and the hierarchical BOA (hBOA). They provide a scalable solution to a broad class of problems. The book provides an overview of evolutionary algorithms that use probabilistic models to guide their search, motivates and describes BOA and hBOA in a way accessible to a wide audience, and presents numerous results confirming that they are revolutionary approaches to black-box optimization.

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Product Details

General

Imprint

Springer-Verlag

Country of origin

Germany

Series

Studies in Fuzziness and Soft Computing, 170

Release date

February 2005

Availability

Expected to ship within 12 - 17 working days

First published

2005

Authors

Dimensions

235 x 155 x 16mm (L x W x T)

Format

Hardcover

Pages

166

Edition

2005 ed.

ISBN-13

978-3-540-23774-7

Barcode

9783540237747

Categories

LSN

3-540-23774-7



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