改进遗传优化的贝叶斯网络结构学习

Structure Learning of BN Based on Improved Genetic Algorithm

针对贝叶斯网络结构学习提出了一种改进的遗传算法,和传统遗传算法相比,该改进算法针对贝叶斯网络结构学习问题增加了优化变异和修正非法图两个新的算子。新算子不但保持了贝叶斯网络学习的多样性和正确性,而且还能保证算法快速搜索到全局最优的网络结构。将该改进遗传算法用于贝叶斯网络结构学习的仿真结果表明,和传统K2算法、GS/GES算法、遗传算法和粒子群算法等算法相比,该算法具有更好的全局搜索能力和收敛速度。

An improved genetic algorithm （IGA） is proposed in this paper for structure learning of Bayesian Network （BN）. Compared with the traditional GA, two new operators named optimized mutation and illegal figure modification are proposed in the improved GA, which aim to solve the BN structure learning problem. The two new operators can simultaneously maintain the diversity and correctness of BN structure learning as well as the algorithm convergence speed of searching the global optimal network structure. In simulation, compared with the traditional algorithms such as K2 algorithm, GS/GES algorithms, normal GA, PSO, etc., the proposed GA shows better performance in global searching and convergence speed.