Graphical model construction based on evolutionary algorithms
Youlong YANG
Yan WU
Sanyang LIU
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.
机标关键词：
分类号：O23(控制论、信息论（数学理论）)
资助基金：国家自然科学基金(60574075)山西省自然科学基金(2005A07)
论文发表日期：2006-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：6( 349-354 )
英文信息