MOA - Markovian Optimisation Algorithm

MOA - Markovian Optimisation Algorithm” by S. Shakya and R. Santana. In Markov Networks in Evolutionary Computation, (S. Shakya and R. Santana, eds.), 2012, pp. 39-53.

Abstract

In this chapter we describe Markovian Optimisation Algorithm (MOA), one of the recent developments in MN based EDA. It uses the local Markov property to model the dependency and directly sample from it without needing to approximate a complex join probability distribution model. MOA has a much simpler workflow in comparison to its global property based counter parts, since expensive processes to finding cliques, and building and estimating clique potential functions are avoided. The chapter is intended as an introductory chapter, and describes the motivation and the workflow of MOA. It also reviews some of the results obtained with it.

BibTeX entry:

@incollection{Shakya_and_Santana:2012b,
   author = {S. Shakya and R. Santana},
   editor = {S. Shakya and R. Santana},
   title = {{MOA} - Markovian Optimisation Algorithm},
   booktitle = {Markov Networks in Evolutionary Computation},
   pages = {39-53},
   publisher = {Springer},
   year = {2012},
   url = {http://dx.doi.org/10.1007/978-3-642-28900-2_3}
}

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