Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms

Description

Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms (Studies in Fuzziness and Soft Computing)

By Martin Pelikan

• Publisher:   Springer
• Number Of Pages:   166
• Publication Date:   2005-03-24
• Sales Rank:   1264444
• ISBN / ASIN:   3540237747
• EAN:   9783540237747
• Binding:   Hardcover
• Manufacturer:   Springer
• Studio:   Springer
• Average Rating:  
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Book 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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