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DEUM - Distribution Estimation Using Markov Networks

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Markov Networks in Evolutionary Computation

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

Abstract

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 differentDEUMalgorithms 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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Correspondence to Siddhartha Shakya .

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Shakya, S., McCall, J., Brownlee, A., Owusu, G. (2012). DEUM - Distribution Estimation Using Markov Networks. In: Shakya, S., Santana, R. (eds) Markov Networks in Evolutionary Computation. Adaptation, Learning, and Optimization, vol 14. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28900-2_4

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  • DOI: https://doi.org/10.1007/978-3-642-28900-2_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28899-9

  • Online ISBN: 978-3-642-28900-2

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