Markov Networks in Evolutionary Computation. 1st ed. 2012

種類:

電子ブック

責任表示:

edited by Siddhartha Shakya, Roberto Santana

出版情報:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012

著者名:

• Shakya, Siddhartha.
• Santana, Roberto.
• SpringerLink (Online service)

シリーズ名:

Adaptation, Learning, and Optimization ; 14

ISBN:

9783642289002 [3642289002]    

注記:

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.
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

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