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.
| ISBN: | 9783540237747 |
| Publication date: | 1st February 2005 |
| Author: | Martin Pelikan |
| Publisher: | Springer an imprint of Springer Berlin Heidelberg |
| Format: | Hardback |
| Pagination: | 166 pages |
| Series: | Studies in Fuzziness and Soft Computing |
| Genres: |
Mathematical theory of computation Artificial intelligence Applied mathematics Maths for engineers Algorithms and data structures Computer programming / software engineering |
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).
Hierarchical Bayesian Optimization Algorithm features in the following genres: Mathematical theory of computation, Artificial intelligence, Applied mathematics, Maths for engineers, Algorithms and data structures, Computer programming / software engineering
Hardback. Not Available.
Hierarchical Bayesian Optimization Algorithm was written by Martin Pelikan and published by Springer an imprint of Springer Berlin Heidelberg
Hierarchical Bayesian Optimization Algorithm has 166 pages
Yes it is part of Studies in Fuzziness and Soft Computing series