The Acyclic Bayesian Net Generator (Student Paper)

Abstract. We present the Acyclic Bayesian Net Generator, a new approach to learn the structure of a Bayesian network using genetic algorithms. Due to the encoding mechanism, acyclicity is preserved through mutation and crossover. We present a detailed description of how our method works and explain why it is better than previous approaches. We can efficiently perform crossover on chromosomes with different node orders without the danger of cycle formation. The approach is capable of learning over all variable node orderings and structures. We also present a proof that our technique of choosing the initial population semi-randomly ensures that the genetic algorithm searches over the whole solution space. Tests show that the method is effective