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中文摘要：
用到模型答应的贝叶斯的网络，从进化算法的当前的人口的解决方案能保证效率；为最佳的智力搜索。然而，到构造，适合给定的数据集的一个贝叶斯的网络是一个 NP 难的问题，；它也需要消费集体计算资源。这篇论文为基于贝叶斯的 Dirichlet 度量标准构造一个图形的模型开发方法论。我们的途径从一套提议被导出；由研究匹配数据集的网络的本地公制的关系的定理。这篇论文论述算法从用上面的答案接近的一套潜力构造一个树模型。这个方法基于图形的模型不仅为进化算法，而且为机器学习是重要的；数据采矿。试验性的结果证明准确理论结果；近似匹配很好。
英文摘要：
Using Bayesian networks to model promising solutions from the current population of the evolutionary algorithms can ensure efficiency and intelligence search for the optimum. However, to construct a Bayesian network that fits a given dataset is a NP-hard problem, and it also needs consuming mass computational resources. This paper develops a methodology for constructing a graphical model based on Bayesian Dirichlet metric. Our approach is derived from a set of propositions and theorems by researching the local metric relationship of networks matching dataset. This paper presents the algorithm to construct a tree model from a set of potential solutions using above approach. This method is important not only for evolutionary algorithms based on graphical models, but also for machine learning and data mining. The experimental results show that the exact theoretical results and the approximations match very well.
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