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Markov Networks in Evolutionary Computation
Markov networks and other probabilistic graphical modes have recently received an upsurge in attention from Evolutionary computation community, particularly in the area of Estimation of distribution algorithms (EDAs). EDAs have arisen as one of the most successful experiences in the application of...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2012.
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| Edición: | 1st ed. 2012. |
| Colección: | Adaptation, Learning, and Optimization,
14 |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 001 | 978-3-642-28900-2 | ||
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| 020 | |a 9783642289002 |9 978-3-642-28900-2 | ||
| 024 | 7 | |a 10.1007/978-3-642-28900-2 |2 doi | |
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| 245 | 1 | 0 | |a Markov Networks in Evolutionary Computation |h [electronic resource] / |c edited by Siddhartha Shakya, Roberto Santana. |
| 250 | |a 1st ed. 2012. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2012. | |
| 300 | |a XX, 244 p. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 490 | 1 | |a Adaptation, Learning, and Optimization, |x 1867-4542 ; |v 14 | |
| 505 | 0 | |a From the content: Probabilistic Graphical Models and Markov Networks -- A review of Estimation of Distribution Algorithms and Markov networks -- MOA - Markovian Optimisation Algorithm -- DEUM - Distribution Estimation Using Markov Networks -- MN-EDA and the use of clique-based factorisations in EDAs -- Convergence Theorems of Estimation of Distribution Algorithms -- Adaptive Evolutionary Algorithm based on a Cliqued Gibbs Sampling over Graphical Markov Model Structure. | |
| 520 | |a Markov networks and other probabilistic graphical modes have recently received an upsurge in attention from Evolutionary computation community, particularly in the area of Estimation of distribution algorithms (EDAs). EDAs have arisen as one of the most successful experiences in the application of machine learning methods in optimization, mainly due to their efficiency to solve complex real-world optimization problems and their suitability for theoretical analysis. This book focuses on the different steps involved in the conception, implementation and application of EDAs that use Markov networks, and undirected models in general. It can serve as a general introduction to EDAs but covers also an important current void in the study of these algorithms by explaining the specificities and benefits of modeling optimization problems by means of undirected probabilistic models. All major developments to date in the progressive introduction of Markov networks based EDAs are reviewed in the book. Hot current research trends and future perspectives in the enhancement and applicability of EDAs are also covered. The contributions included in the book address topics as relevant as the application of probabilistic-based fitness models, the use of belief propagation algorithms in EDAs and the application of Markov network based EDAs to real-world optimization problems. The book should be of interest to researchers and practitioners from areas such as optimization, evolutionary computation, and machine learning. | ||
| 650 | 0 | |a Computational intelligence. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Evolutionary economics. | |
| 650 | 0 | |a Institutional economics. | |
| 650 | 1 | 4 | |a Computational Intelligence. |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Institutional and Evolutionary Economics. |
| 700 | 1 | |a Shakya, Siddhartha. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Santana, Roberto. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783642444944 |
| 776 | 0 | 8 | |i Printed edition: |z 9783642289019 |
| 776 | 0 | 8 | |i Printed edition: |z 9783642288999 |
| 830 | 0 | |a Adaptation, Learning, and Optimization, |x 1867-4542 ; |v 14 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-642-28900-2 |z Texto Completo |
| 912 | |a ZDB-2-ENG | ||
| 912 | |a ZDB-2-SXE | ||
| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||