We address the problem of system reliability prediction, based on an available series of failure time data. We consider support vector regression (SVR) as solution approach, for its known performance on time series forecasting. However, SVR parameters selection is very critical for obtaining satisfactory forecasting. Currently, two different ways are followed to set the values of SVR parameters. One way is that of choosing parameters based on prior knowledge or experts experience on the problem at hand: this is a simple and quick, practical way but often not optimal in complex situations and for non-expert users. Another way is that of searching the values of the parameters via some intelligent methods of optimization of the SVR regression performance: for doing this efficiently, one must avoid problems like divergence, slow convergence, local optima, etc. In this paper, we propose the combination of an analytic selection (AS) method of prior selection followed by a genetic algorithm (GA) for intelligent optimization. The combination of these two methods allows utilizing the available prior knowledge by AS for guiding the GA optimization process so as to avoid divergence and local optima, and accelerate convergence. To show the effectiveness of the method, some simulation experiments are designed, based on artificial or real reliability datasets. The results show the superiority of our proposed ASGA method to the traditional GA method, in terms of prediction accuracy, convergence speed and robustness. 
System reliability prediction by support vector regression with analytic selection and genetic algorithm parameters selection