A Review of Estimation of Distribution Algorithms and Markov Networks

Part of the Adaptation, Learning, and Optimization book series (ALO, volume 14)

Abstract

This chapter reviews some of the popular EDAs based on Markov Networks. It starts by giving introduction to general EDAs and describes the motivation behind their emergence. It then categorises EDAs according to the type of probabilistic models they use (directed model based, undirected model based and common model based) and briefly lists some of the popular EDAs in each categories. It then further focuses on undirected model based EDAs, describes their general workflow and the history, and briefly reviews some of the popular EDAs based on undirected models. It also outlines some of the current research work in this area.

Keywords

Bayesian Network Distribution Algorithm Markov Random Markov Network Probabilistic Graphical Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Business Modelling and Operational Transformation PracticeBT Innovate & DesignIpswichUK
  2. 2.Departamento de Inteligencia ArtificialUniversidad Politécnica de MadridMadridSpain

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