Overview
- Offers a systematic presentation of the use of Markov Networks in Evolutionary Computation
- Fills a void in the current literature on the application of PGMs in evolutionary optimization
- Written by leading experts in the field
Part of the book series: Adaptation, Learning, and Optimization (ALO, volume 14)
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Table of contents (14 chapters)
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Front Matter
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Introduction
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Front Matter
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Theory
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Front Matter
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Back Matter
Keywords
About this book
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.
Editors and Affiliations
Bibliographic Information
Book Title: Markov Networks in Evolutionary Computation
Editors: Siddhartha Shakya, Roberto Santana
Series Title: Adaptation, Learning, and Optimization
DOI: https://doi.org/10.1007/978-3-642-28900-2
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Berlin Heidelberg 2012
Hardcover ISBN: 978-3-642-28899-9Published: 20 April 2012
Softcover ISBN: 978-3-642-44494-4Published: 09 May 2014
eBook ISBN: 978-3-642-28900-2Published: 23 April 2012
Series ISSN: 1867-4534
Series E-ISSN: 1867-4542
Edition Number: 1
Number of Pages: XX, 244
Topics: Computational Intelligence, Artificial Intelligence, Institutional/Evolutionary Economics