Structure learning of Bayesian networks by genetic algorithms: A performance analysis of control parameters
Structure learning of Bayesian networks by genetic algorithms: A performance analysis of control parameters
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DOI：
10.1109/34.537345
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发表时间：
1996-09-01
影响因子：
23.6
通讯作者：
Kuijpers, CMH
中科院分区：
文献类型：
--
作者：
Larranaga, P;Poza, M;Kuijpers, CMH
我们提出了一种新的方法来结构学习领域的贝叶斯网络：我们解决的问题，搜索最佳的贝叶斯网络结构，给定的数据库的情况下，使用遗传算法的哲学之间的搜索替代结构。我们首先假设网络结构的节点之间的排序。这个假设是必要的，以保证网络所创建的遗传算法是法律的贝叶斯网络结构。接下来，我们通过使用“修复运算符”将非法结构转换为法律的结构来释放排序假设。我们提出的实证结果，并进行统计分析。最好的结果是用精英遗传算法，其中包含一个局部优化。
We present a new approach to structure learning in the field of Bayesian networks: We tackle the problem of the search for the best Bayesian network structure, given a database of cases, using the genetic algorithm philosophy for searching among alternative structures. We start by assuming an ordering between the nodes of the network structures. This assumption is necessary to guarantee that the networks that are created by the genetic algorithms are legal Bayesian network structures. Next, we release the ordering assumption by using a ''repair operator'' which converts illegal structures into legal ones. We present empirical results and analyze them statistically. The best results are obtained with an elitist genetic algorithm that contains a local optimizer.