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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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.
Imprint | Springer-Verlag |
Country of origin | Germany |
Series | Studies in Fuzziness and Soft Computing, 170 |
Release date | October 2010 |
Availability | Expected to ship within 10 - 15 working days |
First published | 2005 |
Authors | Martin Pelikan |
Dimensions | 235 x 155 x 10mm (L x W x T) |
Format | Paperback |
Pages | 166 |
Edition | Softcover reprint of hardcover 1st ed. 2005 |
ISBN-13 | 978-3-642-06273-5 |
Barcode | 9783642062735 |
Categories | |
LSN | 3-642-06273-3 |