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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2005.
|
| Edición: | 1st ed. 2005. |
| Colección: | Studies in Fuzziness and Soft Computing,
170 |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 001 | 978-3-540-32373-0 | ||
| 003 | DE-He213 | ||
| 005 | 20220119043713.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 100806s2005 gw | s |||| 0|eng d | ||
| 020 | |a 9783540323730 |9 978-3-540-32373-0 | ||
| 024 | 7 | |a 10.1007/b10910 |2 doi | |
| 050 | 4 | |a QA75.5-76.95 | |
| 072 | 7 | |a UYA |2 bicssc | |
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| 072 | 7 | |a UYA |2 thema | |
| 082 | 0 | 4 | |a 004.0151 |2 23 |
| 100 | 1 | |a Pelikan, Martin. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Hierarchical Bayesian Optimization Algorithm |h [electronic resource] : |b Toward a New Generation of Evolutionary Algorithms / |c by Martin Pelikan. |
| 250 | |a 1st ed. 2005. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2005. | |
| 300 | |a XVIII, 166 p. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Studies in Fuzziness and Soft Computing, |x 1860-0808 ; |v 170 | |
| 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. | |
| 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. | ||
| 650 | 0 | |a Computer science. | |
| 650 | 0 | |a Engineering mathematics. | |
| 650 | 0 | |a Engineering-Data processing. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Computer programming. | |
| 650 | 0 | |a Algorithms. | |
| 650 | 0 | |a Mathematics. | |
| 650 | 1 | 4 | |a Theory of Computation. |
| 650 | 2 | 4 | |a Mathematical and Computational Engineering Applications. |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Programming Techniques. |
| 650 | 2 | 4 | |a Algorithms. |
| 650 | 2 | 4 | |a Applications of Mathematics. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783642062735 |
| 776 | 0 | 8 | |i Printed edition: |z 9783540804994 |
| 776 | 0 | 8 | |i Printed edition: |z 9783540237747 |
| 830 | 0 | |a Studies in Fuzziness and Soft Computing, |x 1860-0808 ; |v 170 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/b10910 |z Texto Completo |
| 912 | |a ZDB-2-ENG | ||
| 912 | |a ZDB-2-SXE | ||
| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||