Partial abductive inference in Bayesian belief networks - An evolutionary computation approach by using problem-specific genetic operators
Abductive inference in Bayesian belief networks (BBNs) is intended as the process of generating the K most probable configurations given observed evidence. When we are interested only in a subset of the network's variables, this problem is called partial abductive inference. Both problems are NP-hard and so exact computation is not always possible. In this paper, a genetic algorithm is used to perform partial abductive inference in BBNs. The main contribution is the introduction of new genetic operators designed specifically for this problem. By using these genetic operators, we try to take advantage of the calculations previously carried out, when a new individual is evaluated. The algorithm is tested using a widely used Bayesian network and a randomly generated one and then compared with a previous genetic algorithm based on classical genetic operators. From the experimental results, we conclude that the new genetic operators preserve the accuracy of the previous algorithm, and also reduce the number of operations performed during the evaluation of individuals. The performance of the genetic algorithm is, thus, improved.
abductive inference bayesian belief networks evolutionary computation genetic operators most probable explanation probabilistic reasoning