File(s) under permanent embargo

Reason: This item is currently closed access.

DEUM: Distribution Estimation Using Markov

chapter
posted on 25.10.2011, 13:45 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.

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

© Springer

Version

VoR (Version of Record)

Publication date

2012

Notes

This book chapter is in closed access, it will be published in Markov Networks in Evolutionary Computation [© Springer: May 2012].

ISBN

9783642288999

Language

en

Exports

Logo branding

Keywords

Exports