Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms / Edition 1

by Martin Pelikan

Overview

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

ISBN-13:	9783642062735
Publisher:	Springer Berlin Heidelberg
Publication date:	12/15/2010
Series:	Studies in Fuzziness and Soft Computing , #170
Edition description:	Softcover reprint of hardcover 1st ed. 2005
Pages:	166
Product dimensions:	6.10(w) x 9.25(h) x 0.02(d)

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.