Building a GA from Design Principles for Learning Bayesian Networks

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.

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