Learning Causality Diagram Structure Based on Genetic Algorithm
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    Abstract:

    The Causality Diagram theory,which adopted graphical expression of knowledge and direct causality intensity of causality,overcomes some shortages in Belief Network and has evolved into a mixed causality diagram methodology coped with discrete and continuous variable.But it is difficult that the structure of Causality Diagram given by expert.Because the complexity of causality diagram structure goes up exponentially through the number of the vertex's increasing,it is NP-hard problem to find the most possible structure from a set of data.The authors discuss approaches and present Genetic Algorithm,to find the most possible structure from a set of data.Experiment shows the method is effective.

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石庆喜 梁新元 张勤.基于遗传算法的因果图网络结构学习[J].重庆大学学报,2006,29(4):111~114

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  • Received:December 07,2005
  • Revised:December 07,2005
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