Hierarchical Bayesian Optimization Algorithm
Toward a New Generation of Evolutionary Algorithms
(Sprache: Englisch)
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...
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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 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.
Inhaltsverzeichnis zu „Hierarchical Bayesian Optimization Algorithm “
- From Genetic Variation to Probabilistic Modeling- Probabilistic Model-Building Genetic Algorithms
- Bayesian Optimization Algorithm
- Scalability Analysis
- The Challenge of Hierarchical Difficulty
- Hierarchical Bayesian Optimization Algorithm
- Hierarchical BOA in the Real World.
Bibliographische Angaben
- Autor: Martin Pelikan
- 2005, 166 Seiten, Maße: 16,2 x 24,2 cm, Gebunden, Englisch
- Verlag: Springer
- ISBN-10: 3540237747
- ISBN-13: 9783540237747
Sprache:
Englisch
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