Overview
- Presents a new generation of evolutionary algorithms, which are revolutionary approaches to black-box optimization
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 170)
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Table of contents (8 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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.
Bibliographic Information
Book Title: Hierarchical Bayesian Optimization Algorithm
Book Subtitle: Toward a New Generation of Evolutionary Algorithms
Authors: Martin Pelikan
Series Title: Studies in Fuzziness and Soft Computing
DOI: https://doi.org/10.1007/b10910
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2005
Hardcover ISBN: 978-3-540-23774-7Published: 01 February 2005
Softcover ISBN: 978-3-642-06273-5Published: 21 October 2010
eBook ISBN: 978-3-540-32373-0Published: 16 March 2005
Series ISSN: 1434-9922
Series E-ISSN: 1860-0808
Edition Number: 1
Number of Pages: XVIII, 166
Topics: Theory of Computation, Mathematical and Computational Engineering, Artificial Intelligence, Programming Techniques, Algorithms, Applications of Mathematics