An EDA based on local Markov property and Gibbs sampling

An EDA based on local Markov property and Gibbs sampling” by Siddhartha Shakya and R. Santana. In Proceedings of the 2008 Genetic and evolutionary computation conference (GECCO), (Maarten Keijzer, ed.), (New York, NY, USA), 2008, pp. 475-476.

Abstract

The key ideas behind most of the recently proposed Markov networks based EDAs were to factorise the joint probability distribution in terms of the cliques in the undirected graph. As such, they made use of the global Markov property of the Markov network. Here we presents a Markov Network based EDA that exploits Gibbs sampling to sample from the Local Markov property, the Markovianity, and does not directly model the joint distribution. We call it Markovianity based Optimisation Algorithm. Some initial results on the performance of the proposed algorithm shows that it compares well with other Bayesian network based EDAs

BibTeX entry:

@inproceedings{Shakya_and_Santana:2008,
   author = {Siddhartha Shakya and R. Santana},
   editor = {Maarten Keijzer},
   title = {An {EDA} based on local {M}arkov property and {G}ibbs sampling},
   booktitle = {Proceedings of the 2008 Genetic and evolutionary
	computation conference (GECCO)},
   pages = {475--476},
   publisher = {ACM},
   address = {New York, NY, USA},
   year = {2008},
   url = {http://dl.acm.org/citation.cfm?id=1389185}
}

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