Aiming at the difficulty in good segments of the formula to be inherited in formula discovering using gene expression programming (GEP), this paper proposes an innovative immune formula discovering algorithm (IFDA), which is actually inspired by MHC (major histocompatibility complex) regulation principle of immune theory. In IFDA, the formula are mapped as a tree structure and transformed into both constant and variation section of antibody with a depth-first mechanism while its fragment is encoded into the MHC. By the feature of MHC regulation, IFDA mines the dataset to discover the proper formula very quickly. Many data are benchmarked for verifying the performance of IFDA in which all results from experiments show that the IFDA can really provide better performance than GEP in both convergence speed and formula complexity.