Abstract
Partial abductive inference in Bayesian networks is intended as the pro-cess of generating the K most probable configurations for a distinguished subset of the network variables (explanation set), given some observations (evidence). This problem, also known as the Maximum a Posteriori Problem, is known to be NP-hard, so exact computation is not always possible. As partial abductive inference in Bayesian networks can be viewed as a combinatorial optimization problem, Genetic Algorithms have been successfully applied to give an approximate algorithm for it (de Campos et al., 1999). In this work we approach the problem by means of Estimation of Distribution Algorithms, and an empirical comparison between the results obtained by Genetic Algorithms and Estimation of Distribution Algorithms is carried out.
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Campos, L.M., Gámez, J.A., Larrañaga, P., Moral, S., Romero, T. (2002). Partial Abductive Inference in Bayesian Networks: An Empirical Comparison Between GAs and EDAs. In: Larrañaga, P., Lozano, J.A. (eds) Estimation of Distribution Algorithms. Genetic Algorithms and Evolutionary Computation, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1539-5_16
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DOI: https://doi.org/10.1007/978-1-4615-1539-5_16
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