A hybrid approach to learn Bayesian networks using evolutionary programming

Abstract

A novel hybrid framework is reported that improves upon our previous work, MDLEP, which uses evolutionary programming to solve the difficult Bayesian network learning problem. A new merge operator is also introduced that further enhances the efficiency. As experimental results suggest, our hybrid approach performs significantly better than MDLEP.