Bayesian network structure learning with improved genetic algorithm

As an important model of machine learning, Bayesian networks (BNs) have received a lot of attentions since they can be used for classification via probabilistic inference. However, since it is a complicated combination optimization problem, BN structure learning cannot be solved with classic convex optimization algorithms. Hence, evolutionary algorithms provide an alternative way to find a global solution to BN structure learning problem. In this paper, we improve the biased random-key genetic algorithm to solve the BN structure learning problem. Meanwhile, we apply a local optimization model as its decoder to improve the performance of the proposed algorithm. Finally, we conduct our experiments on nine benchmark networks and a real dataset of cross-site scripting (XSS) attack. Experimental results show that the proposed algorithm can obtain more accurate solutions than other state-of-the-art algorithms and achieve a good performance in XSS attack detection for web security.

Automated summary

This paper proposes an improved biased random-key genetic algorithm to solve the BN structure learning problem. A local optimization model is applied as its decoder to enhance the algorithm's performance. Experimental results demonstrate that the proposed algorithm achieves better accuracy than other state-of-the-art algorithms and performs well in XSS attack detection for web security.