Hierarchical Bayesian Optimization Algorithm : Toward a New Generation of Evolutionary Algorithms
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 book focuses on two algorithms that replace traditional variation operators of evolutionary algorithms by learning a...
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| Главный автор: | |
|---|---|
| Формат: | Livre numérique |
| Язык: | Anglais |
| Опубликовано: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
2005.
Cham : Springer Nature |
| Серии: | Studies in Fuzziness and Soft Computing
170 |
| Online-ссылка: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Примечание: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Hierarchical Bayesian Optimization Algorithm, Texte imprimé, 9783642062735 • Hierarchical Bayesian Optimization Algorithm, Texte imprimé, 9783540804994 • Hierarchical Bayesian Optimization Algorithm, Texte imprimé, 9783540237747 |
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| 100 | 1 | |a Pelikan, Martin. | |
| 245 | 1 | 0 | |a Hierarchical Bayesian Optimization Algorithm : |b Toward a New Generation of Evolutionary Algorithms |c edited by Martin Pelikan. |
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c 2005. | ||
| 490 | 0 | |a Studies in Fuzziness and Soft Computing |v 170 |x 1860-0808 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 0 | |a 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. | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. chttps://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a 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 book focuses 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). BOA and hBOA are theoretically and empirically shown to provide robust and scalable solution for broad classes of nearly decomposable and hierarchical problems. A theoretical model is developed that estimates the scalability and adequate parameter settings for BOA and hBOA. The performance of BOA and hBOA is analyzed on a number of artificial problems of bounded difficulty designed to test BOA and hBOA on the boundary of their design envelope. The algorithms are also extensively tested on two interesting classes of real-world problems: MAXSAT and Ising spin glasses with periodic boundary conditions in two and three dimensions. Experimental results validate the theoretical model and confirm that BOA and hBOA provide robust and scalable solution for nearly decomposable and hierarchical problems with only little problem-specific information. | ||
| 776 | 0 | |t Hierarchical Bayesian Optimization Algorithm |b Texte imprimé |z 9783642062735 | |
| 776 | 0 | |t Hierarchical Bayesian Optimization Algorithm |b Texte imprimé |z 9783540804994 | |
| 776 | 0 | |t Hierarchical Bayesian Optimization Algorithm |b Texte imprimé |z 9783540237747 | |
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