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DEUM: Distribution Estimation Using Markov
chapter
posted on 2011-10-25, 13:45 authored by Siddhartha Shakya, John McCall, Sandy Brownlee, Gilbert OwusuDEUM 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.
History
School
- Architecture, Building and Civil Engineering
Citation
SHAKYA, 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.Publisher
© SpringerVersion
- VoR (Version of Record)
Publication date
2012Notes
This book chapter is in closed access, it will be published in Markov Networks in Evolutionary Computation [© Springer: May 2012].ISBN
9783642288999Language
- en