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Summary: Performance Analysis of an Acyclic Genetic
approach to Learn Bayesian Network Structure
(Student Paper)
Pankaj B. Gupta1
and Vicki H. Allan2
1
Microsoft Corporation, One Microsoft Way, Redmond, WA 98052, USA,
pagupta@microsoft.com
2
Computer Science Department, Utah State University, Logan, UT 84322,
allanv@cs.usu.edu
Abstract. We introduce a new genetic algorithm approach for learn-
ing a Bayesian network structure from data. Our method is capable of
learning over all node orderings and structures. Our encoding scheme is
inherently acyclic and is capable of performing crossover on chromosomes
with different node orders. We present an analysis of this approach using
different Bayesian networks such as ASIA and ALARM. Results sug-
gest that the method is effective. The tests we perform include varying
the population size of the genetic algorithms, restricting the maximum
number of parents a node can have, and learning with a fixed node order.
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