Performance Analysis of an Acyclic Genetic approach to Learn Bayesian Network Structure

Abstract. We introduce a new genetic algorithm approach for learning a Bayesian network structure from data. Our method is capable of learning over all node orderings and structures. Our encoding scheme is inherently acyclic and is capable of performing crossover on chromosomes with different node orders. We present an analysis of this approach using different Bayesian networks such as ASIA and ALARM. Results suggest that the method is effective. The tests we perform include varying the population size of the genetic algorithms, restricting the maximum number of parents a node can have, and learning with a fixed node order.

• Type: Conference
• Year: 2003
• Where: IICAI
• Authors: Pankaj B. Gupta, Vicki H. Allan
• Keywords: Bayesian Networks, IICAI 2003, IICAI 2007, Node, Node Order