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|Title: ||Applications of distribution estimation using markov network modelling (DEUM)|
|Authors: ||McCall, John|
Brownlee, Alexander E.I.
|Issue Date: ||2012|
|Publisher: ||© Springer|
|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.|
|Abstract: ||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
|Description: ||This book chapter is in closed access, it will be published in Markov Networks in Evolutionary Computation [© Springer: May 2012].|
|Appears in Collections:||Closed Access (Architecture, Building and Civil Engineering)|
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