Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:310-316, 1995.
This paper demonstrates how Genetic Algorithms can be used to discover the structure of a Bayesian Network from a given database with cases. The results presented, were obtained by applying four different types of Genetic Algorithms - SSGA (Steady State Genetic Algorithm), GAe $\lambda$ (Genetic Algorithm elistist of degree $\lambda$ ), hSSGA (hybrid Steady State Genetic Algorithm) and the hGAe $\lambda$ (hybrid Genetic Algorithm elitist of degree $\lambda$ ) - to simulations of the ALARM Network. The behaviour of the mentioned algorithms is studied with respect to their parameters.