@inproceedings{c7a399e3931043c8acabce11f092fc09,
title = "An EDA based on local Markov property and Gibbs sampling",
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.",
keywords = "Estimation of distribution algorithms, Evolutionary computation, Markov networks, Probabilistic graphical models",
author = "Siddhartha Shakya and Roberto Santana",
year = "2008",
doi = "10.1145/1389095.1389185",
language = "British English",
isbn = "9781605581309",
series = "GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008",
publisher = "Association for Computing Machinery (ACM)",
pages = "475--476",
booktitle = "GECCO'08",
address = "United States",
note = "10th Annual Genetic and Evolutionary Computation Conference, GECCO 2008 ; Conference date: 12-07-2008 Through 16-07-2008",
}