Competing Mutating Agents for Bayesian Network Structure Learning

Search and score techniques have been widely applied to the problem of learning Bayesian Networks (BNs) from data. Many implementations focus on finding an ordering of variables from which edges can be inferred. Although varying across data, most search spaces for such tasks exhibit many optima and plateaus. Such characteristics represent a trap for population-based algorithms as the diversity decreases and the search converges prematurely. In this paper, we study the impact of a distance mutation operator and propose a novel method using a population of agents that mutate their solutions according to their respective positions in the population. Experiments on a set of benchmark BNs confirm that diversity is maintained throughout the search. The proposed technique shows improvement on most of the datasets by obtaining BNs of similar of higher quality than those obtained by Genetic Algorithm methods.