Book Chapter

Applications of distribution estimation using Markov Network Modelling (DEUM)



McCall J, Brownlee A & Shakya S (2012) Applications of distribution estimation using Markov Network Modelling (DEUM). In: Shakya S & Santana R (eds.) Markov Networks in Evolutionary Computation. Adaptation, Learning, and Optimization, 14. Berlin Heidelberg: Springer, pp. 193-207.

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.

Title of seriesAdaptation, Learning, and Optimization
Number in series14
Publication date31/12/2012
Publisher URL…3-642-28900-2_12
Place of publicationBerlin Heidelberg
ISSN of series1867-4534

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Dr Sandy Brownlee

Dr Sandy Brownlee

Senior Lecturer in Computing Science, Computing Science and Mathematics - Division