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Beschreibung
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.
Toward a New Generation of Evolutionary Algorithms
Details
| Verlag | Springer Berlin |
| Ersterscheinung | 21. Oktober 2010 |
| Maße | 23.5 cm x 15.5 cm |
| Gewicht | 289 Gramm |
| Format | Softcover |
| ISBN-13 | 9783642062735 |
| Seiten | 166 |
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