The traditional SGA has the characters of converging early and obtaining easily local best result in the case of processing complicated optimizing problem. So the immune principle is brought in SGA and a new affinity definition strategy (affinity based on sine function) is put forward. This strategy restrains adaptability in the manner of approximate line prophase and flatness anaphase, and a sine based immune genetic algorithm (SIGA) is designed to improve its global and local searching abilities. The experiment results demonstrate that the convergent precision and speed of SIGA is better than those of SGA. Taking the engine stabilization for example, by applying the SIGA, picking up the aviation state classification rules is successfully realized. The testing results indicate that the rule acquired is simple and effective if the training sample is selected properly.