On the use of local search heuristics to improve GES-based Bayesian network learning

Other algorithms search for the networks in the space of equivalence classes. The most important of these is GES (greedy equivalence search). It guarantees obtaining the optimal network under certain conditions. However, it can also get stuck in local optima when learning from datasets with limited size. This article proposes the use of local search-based metaheuristics as a way to improve the behaviour of GES in such circumstances. These methods also guarantee asymptotical optimality, and the experiments show that they improve upon the score of the networks obtained with GES.