ABDUCTIVE REASONING IN BAYESIAN BELIEF NETWORKS USING A GENETIC ALGORITHM

被引：24

作者：

GELSEMA, ES

机构：

[1] Department of Medical Informatics, Erasmus University, 3000 DR Rotterdam

来源：

PATTERN RECOGNITION LETTERS | 1995年 / 16卷 / 08期

关键词：

BAYESIAN BELIEF NETWORKS; GENETIC ALGORITHMS; ABDUCTIVE REASONING;

D O I：

10.1016/0167-8655(95)00046-J

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A set of computational experiments is described in which genetic algorithms are used for abductive reasoning in Bayesian belief networks. It is shown that good solutions and explanations are consistently found with high probabilities. The efficiency of genetic sampling w.r.t. random sampling is shown to increase with increasing complexity of the search space and with increasing complexity of the search goal.

引用

页码：865 / 871

页数：7

共 7 条

[1] THE COMPUTATIONAL-COMPLEXITY OF PROBABILISTIC INFERENCE USING BAYESIAN BELIEF NETWORKS [J].