Markov Networks in Evolutionary Computation

. Ed(s): Shakya, Siddhartha; Santana, Roberto

ISBN 10: 3642444946 / ISBN 13: 9783642444944

Verlag: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, 2014

This book focuses on the different steps involved in the conception, implementation and application of Estimation of distribution algorithms (EDAs) that use Markov networks and undirected models in general. Editor(s): Shakya, Siddhartha; Santana, Roberto. Series: Adaptation, Learning, and Optimization. Num Pages: 264 pages, biography. BIC Classification: PBUD; UYQ. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 14. Weight in Grams: 409. . 2014. Paperback. . . . . Books ship from the US and Ireland. Bestandsnummer des Verkäufers V9783642444944

Inhaltsangabe:

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.

Von der hinteren Coverseite:

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.

Bibliografische Details

Titel: Markov Networks in Evolutionary Computation
Verlag: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Erscheinungsdatum: 2014
Einband: Softcover
Zustand: New