پیش بینی قابلیت اطمینان سیستم توسط رگرسیون بردار پشتیبان با انتخاب تحلیلی و پارامترهای الگوریتم ژنتیک انتخاب

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.