Building a GA from design principles for learning Bayesian networks

Authors:
Steven van Dijk;Dirk Thierens;Linda C. van der Gaag
Affiliations:
Universiteit Utrecht, Institute of Information and Computing Sciences, Decision Support Systems, Utrecht, The Netherlands;Universiteit Utrecht, Institute of Information and Computing Sciences, Decision Support Systems, Utrecht, The Netherlands;Universiteit Utrecht, Institute of Information and Computing Sciences, Decision Support Systems, Utrecht, The Netherlands
Venue:
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Year:
2003

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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.