LSevoBN: a structure learning algorithm for large Bayesian networks

The issue of structure learning in large Bayesian networks has gained significant attention from the academic community in recent years due to its wide-ranging applications in various fields. Efficient algorithms for structure learning are crucial for enabling the practical use of Bayesian networks in real-world scenarios where the number of variables can be substantial. In previous approaches, addressing this problem has resulted in either prolonged convergence times or significant degradation of the problem. Although evolutionary algorithms have been effective in training Bayesian Networks (BNs), their application to large BNs has been impractical due to lengthy convergence times. In this paper, we introduce a modified evolutionary algorithm for training large BNs by dividing the network into smaller ones, coding and connecting them through evolutionary optimization. Our proposed approach significantly accelerates the training process, achieving a time acceleration of more than twenty times compared to existing solutions, while maintaining comparable or slightly degraded quality.