Abstract
Recent developments in GA theory have given rise to a number of design principles that serve to guide the construction of selectorecombinative 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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van Dijk, S., Thierens, D., van der Gaag, L.C. (2003). Building a GA from Design Principles for Learning Bayesian Networks. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45105-6_101
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DOI: https://doi.org/10.1007/3-540-45105-6_101
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