An incremental approach to lear ning Bayesian networks based on genetic algorithm

namely ILBN

is put forward in this paper.ILBN introduces the EM algorithm and genetic algorithm into the incremental process of Bayesian network lear ning

calculates the expectation of the sufficient statistics with incomplete data using EM algorithm and evolves network structures using genetic algorithm

that could avoid getting into local maxima to some extent.Furthermore

by defining a new mutation operator and extending the traditional crossover operator

ILBN could incrementally lear n and evolve Bayesian networks containing hidden variables.Finally

ILBN improves the incremental process by Friedman et al.The experimental results show that

in terms of storage cost

ILBN is comparable with the method by Friedman et al

while under the same experimental conditions

ILBN could lear n more accurate networks than that of Friedman et al.The experimental results also verify the validity of ILBN in presence of incomplete data and hidden variables.