Learning Bayesian networks structures based on extending evolutionary programming

This paper describes a new data mining algorithm to learn Bayesian networks structures based on an extending evolutionary programming (EP) method and the minimum description length (MDL) principle. Aiming at preventing and overcoming the premature convergence, the algorithm combines the niche technology into the selection mechanism of EP. In addition, our algorithm, like some previous work, does not need to have a complete variable ordering as input. To evaluate the performance of our algorithm, we conduct a series of experiments and compare them with the previous work based on genetic algorithms (GA). The experimental results illustrate that both the quality of the solutions and computational time of our algorithm are superior.