A Genetic Algorithm for Learning Bayesian Network Adjacency Matrices
نویسندگان
چکیده
In this thesis, we provide a general background for inference and learning, using Bayesian networks and genetic algorithms. We introduce Bayesian Networks in Java, a Java-based Bayesian network API that we have developed. We describe our research with structure learning using a genetic algorithm to search the space of adjacency matrices for a Bayesian network. We first instantiate the population using one of several methods: pure random sampling, perturbation or refinement of a candidate network produced using the Sparse Candidate algorithm of Friedman et al., and the aggregate output of Cooper and Herskovits’ K2 algorithm applied to one or more random node ordering. We evaluate the genetic algorithm using well-known networks such as Asia and Alarm, and show that it is an effective structure-learning algorithm. A GENETIC ALGORITHM FOR LEARNING BAYESIAN NETWORK ADJACENCY MATRICES FROM DATA by BENJAMIN B. PERRY B.S., Kansas State University, 2002 A THESIS submitted in partial fulfillment of the requirements for the degree MASTER OF SCIENCE Department of Computing and Information Science College of Engineering KANSAS STATE UNIVERSITY Manhattan, Kansas
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