In order to predict more accurately the pressure transients accompanying air release and vaporous cavitation inside oil-hydraulic low pressure pipelines, a new method using genetic algorithms (GAs) for parameter identification is described. A mathematical model for pressure and flow transients is presented in which models of vaporous cavitation and dynamic air release and re-solution are incorporated. This model enables the prediction of both the vaporous cavitation and the air bubble volumes in the pipeline during the transients following a sudden cut-off of the flow. The accurate prediction of behavior largely depends on three generally unknown parameters required by the model, namely: the initial air bubble volume in the oil, and the air release and re-solution time constants. Through the use of the GAs, these parameters can be identified. Predicted results and experimental data show close correspondence.