Applications of distribution estimation using Markov Network Modelling (DEUM)

John McCall, Alexander Brownlee, Siddhartha Shakya

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

4 Scopus citations


In recent years, Markov Network EDAs have begun to find application to a range of important scientific and industrial problems. In this chapter we focus on several applications of Markov Network EDAs classified under the DEUM framework which estimates the overall distribution of fitness from a bitstring population. In Section 1 we briefly review the main features of the DEUM framework and highlight the principal features that havemotivated the selection of applications. Sections 2 - 5 describe four separate applications: chemotherapy optimisation; dynamic pricing; agricultural biocontrol; and case-based feature selection. In Section 6 we summarise the lessons learned from these applications. These include: comparisons with other techniques such as GA and Bayesian Network EDAs; trade-offs between modelling cost and reduction in search effort; and the use of MN models for surrogate evaluation.We also present guidelines for further applications and future research.

Original languageBritish English
Title of host publicationMarkov Networks in Evolutionary Computation
PublisherSpringer Verlag
Number of pages15
ISBN (Print)9783642288999
StatePublished - 2012

Publication series

NameAdaptation, Learning, and Optimization
ISSN (Print)1867-4534
ISSN (Electronic)1867-4542


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