Hierarchical bayesian optimization algorithm : toward a new generation of evolutionary algorithms
Author(s)
Bibliographic Information
Hierarchical bayesian optimization algorithm : toward a new generation of evolutionary algorithms
（Studies in fuzziness and soft computing, v. 170）
Springer, c2005
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Note
Includs bibliographical references (p. [151]-161) and index
Description and Table of Contents
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.
Table of Contents
From Genetic Variation to Probabilistic Modeling.- Probabilistic Model-Building Genetic Algorithms.- Bayesian Optimization Algorithm.- Scalability Analysis.- The Challenge of Hierarchical Difficulty.- Hierarchical Bayesian Optimization Algorithm.- Hierarchical BOA in the Real World.
by "Nielsen BookData"