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| DOI： |
| Received:October 08, 2005Revised:June 01, 2006 |
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| Graphical model construction based on evolutionary algorithms |
| Youlong YANG, Yan WU, Sanyang LIU |
| (Department of Mathematical Sciences, Xidian University, Xi’an Shannxi 710071, China) |
| Abstract: |
| 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. |
| Key words: Graphical model Evolutionary algorithms Bayesian network Tree models Bayesian Dirichlet metric |
