Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms - Martin Pelikan
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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 ... Full description
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Description
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.
More Information
| Author | Martin Pelikan |
|---|---|
| Publisher | Springer Berlin Heidelberg |
| Series | Studies in Fuzziness and Soft Computing |
| Release year | 2010 |
| Cover type | Softcover |
| EAN | 9783642062735 |