Adaptation, Learning, and Optimization

Markov Networks in Evolutionary Computation

Editors: Shakya, Siddhartha, Santana, Roberto (Eds.)

  • 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
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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.

Table of contents (14 chapters)

  • Probabilistic Graphical Models and Markov Networks

    Santana, Roberto (et al.)

    Pages 3-19

  • A Review of Estimation of Distribution Algorithms and Markov Networks

    Shakya, Siddhartha (et al.)

    Pages 21-37

  • MOA - Markovian Optimisation Algorithm

    Shakya, Siddhartha (et al.)

    Pages 39-53

  • DEUM - Distribution Estimation Using Markov Networks

    Shakya, Siddhartha (et al.)

    Pages 55-71

  • MN-EDA and the Use of Clique-Based Factorisations in EDAs

    Santana, Roberto

    Pages 73-87

Buy this book

eBook $139.00
price for USA (gross)
  • ISBN 978-3-642-28900-2
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $179.00
price for USA
  • ISBN 978-3-642-28899-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $179.00
price for USA
  • ISBN 978-3-642-44494-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Rent the ebook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
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Bibliographic Information

Bibliographic Information
Book Title
Markov Networks in Evolutionary Computation
Editors
  • Siddhartha Shakya
  • Roberto Santana
Series Title
Adaptation, Learning, and Optimization
Series Volume
14
Copyright
2012
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer Berlin Heidelberg
eBook ISBN
978-3-642-28900-2
DOI
10.1007/978-3-642-28900-2
Hardcover ISBN
978-3-642-28899-9
Softcover ISBN
978-3-642-44494-4
Series ISSN
1867-4534
Edition Number
1
Number of Pages
XX, 244
Topics