A novel strategy for dynamic risk grading of major hazards based on evolutionary algorithm

In this paper, we present a novel evolutionary algorithm for dynamic risk grading of major hazards, which can be called DRGEA for short. And we detail the construction theory of DRGEA from many aspects such as individual encoding, evolutionary operators, fitness function and so forth. In the numerical experiment, DRGEA is used to solve a practical problem about dynamic risk grading of major hazards. The results of simulation experiments show that DRGEA has better performances, compared with other classification methods.