A parallel hybrid genetic algorithm simulated annealing approach to finding most probable explanations on Bayesian belief networks

Bayesian belief networks are an important knowledge structure for reasoning under uncertainty. In the most probable explanation (MPE) problem, also known as the maximum a-posteriori (MAP) assignment problem, the objective is to assign truth values to network variables in a way that will maximize their joint probability conditioned on the evidence to be explained. This problem has recently been shown to be NP-hard for general belief networks and for large networks, exact solution methods are not practical. In this paper, we present a parallel processing technique, particularly suitable for loosely-coupled multicomputers which combines genetic algorithms with simulated annealing. This method is applied to the MPE problem on Bayesian belief network and is found to be superior on the MPE problem to either genetic algorithms or simulated annealing separately