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Applications of distribution estimation using markov network modelling (DEUM)

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posted on 25.10.2011, 13:55 by John McCall, Sandy Brownlee, Siddhartha Shakya
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.

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  • Architecture, Building and Civil Engineering

Citation

MCCALL, J., BROWNLEE, A.E.I. and SHAKYA, S., 2012. Applications of distribution estimation using markov network modelling (DEUM). 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

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