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

~ (Author) "Genetic algorithms (GAs) [53, 83] are stochastic optimization methods inspired by natural evolution and genetics..." (more)
Key Phrases: hierarchical traps, trap partition, scalability theory, Probabilistic Modeling, Structure Level, Tournaments Selected (more...)

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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.

Product Details

• Hardcover: 166 pages
• Publisher: Springer; 1 edition (March 24, 2005)
• Language: English
• ISBN-10: 3540237747
• ISBN-13: 978-3540237747
• Product Dimensions: 9.4 x 6.3 x 0.6 inches
• Shipping Weight: 13.6 ounces (View shipping rates and policies)
• Average Customer Review: No customer reviews yet. Be the first.
• Amazon.com Sales Rank: #1,965,731 in Books (See Bestsellers in Books)

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