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Building a GA from Design Principles for Learning Bayesian Networks
Steven van Dijk, Dirk Thierens, and Linda C. van der Gaag
Universiteit Utrecht,
Institute of Information and Computing Sciences,
Decision Support Systems,
PO Box 80.089,
3508 TB Utrecht, TheNetherlands
{steven,dirk,linda}@cs.uu.nl
Abstract. Recent developments in GA theory have given rise to a number of design principles that serve to guide the construction of selecto-recombinative GAs from which good performance can be expected. In this paper, we demonstrate their application to the design of a GA for a well-known hard problem in machine learning: the construction of a Bayesian network from data. We show that the resulting GA is able to efficiently and reliably find good solutions. Comparisons against state-of-the-art learning algorithms, moreover, are favorable.
LNCS 2723, p. 886 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003