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DEUM: Distribution Estimation Using Markov
chapterposted on 2011-10-25, 13:45 authored by Siddhartha Shakya, John McCall, Sandy Brownlee, Gilbert Owusu
DEUM is one of the early EDAs to use Markov Networks as its model of probability distribution. It uses undirected graph to represent variable interaction in the solution, and builds a model of fitness function from it. The model is then fitted to the set of solutions to estimate the Markov network parameters; these are then sampled to generate new solutions. Over the years, many different DEUMalgorithms have been proposed. They range from univariate version that does not assume any interaction between variables, to fully multivariate version that can automatically find structure and build fitness models. This chapter serves as an introductory text on DEUM algorithm. It describes the motivation and the key concepts behind these algorithms. It also provides workflow of some of the key DEUM algorithms.
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CitationSHAKYA, S., MCCALL, J., BROWNLEE, A.E.I., and OWUSU, G., 2012. DEUM: Distribution Estimation Using Markov. IN: Shakya, S. and Santana, R. (Eds.) Markov Networks in Evolutionary Computation. Adaptation, Learning, and Optimization, Vol. 14. London: Springer.
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NotesThis book chapter is in closed access, it will be published in Markov Networks in Evolutionary Computation [© Springer: May 2012].