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.
| Autorius: | Martin Pelikan |
| Serija: | Studies in Fuzziness and Soft Computing |
| Leidėjas: | Springer Berlin Heidelberg |
| Išleidimo metai: | 2010 |
| Knygos puslapių skaičius: | 184 |
| ISBN-10: | 3642062733 |
| ISBN-13: | 9783642062735 |
| Formatas: | Knyga minkštu viršeliu |
| Kalba: | Anglų |
| Žanras: | Applied mathematics |
Parašykite atsiliepimą apie „Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms“
