| 書目名稱 | Hierarchical Bayesian Optimization Algorithm |
| 副標(biāo)題 | Toward a New Generat |
| 編輯 | Martin Pelikan |
| 視頻video | http://file.papertrans.cn/427/426125/426125.mp4 |
| 概述 | Presents a new generation of evolutionary algorithms, which are revolutionary approaches to black-box optimization.Includes supplementary material: |
| 叢書名稱 | Studies in Fuzziness and Soft Computing |
| 圖書封面 |  |
| 描述 | .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.. |
| 出版日期 | Book 2005 |
| 關(guān)鍵詞 | Analysis; Bayesian network; algorithm; algorithms; evolutionary algorithm; genetic algorithms; learning; ma |
| 版次 | 1 |
| doi | https://doi.org/10.1007/b10910 |
| isbn_softcover | 978-3-642-06273-5 |
| isbn_ebook | 978-3-540-32373-0Series ISSN 1434-9922 Series E-ISSN 1860-0808 |
| issn_series | 1434-9922 |
| copyright | Springer-Verlag Berlin Heidelberg 2005 |
|Archiver|手機(jī)版|小黑屋|
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