Towards a More Efficient Evolutionary Induction of Bayesian Networks

C. Cotta, J. Muruzábal

Parallel Problem Solving From Nature VII, J.J. Merelo et al. (eds.), Lecture Notes in Computer Science 2439, pp. 730-739, Springer-Verlag Berlin, 2002 (PPSN'04 best paper award)

© Springer-Verlag Berlin Heidelberg 2002. All rights reserved.


Bayesian networks (BNs) constitute a useful tool to model the joint distribution of a set of random variables of interest. This paper is concerned with the network induction problem. We propose a number of hybrid recombination operators for extracting BNs from data. These hybrid operators make use of phenotypic information in order to guide the processing of information during recombination. The performance of these new operators is analyzed with respect to that of their genotypic counterparts. It is shown that these hybrid operators provide notably improved and rather robust results. Some remarks on the future of the area are also laid out.

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