Abstract: Based on the current immune evolutionary algorithm ( IEA) and aimed at interval- constrained problems, a universal immune evolutionary algorithm was proposed. W ith this universal algorithm, any individualofevery generation canmeet the requirementof interval constraintsbymeans of interval transition, and the subjectivity ofparameters setting can be further eliminated. As a resul,t the computation efficiency is greatly raised, the unitarity of the universal algorithm is improved, and the disadvantages of the IEA adopting penalty for similar problems are also avoided. In addition, the universal algorithm was applied to the optimization ofmulti-model functions and the test of a GA (genetic algorithm) deceptive problem. The results show that compared with the IEA adopting penalty, programming is easy for the universal algorithm, and the global optimal solution can be obtained at a fast and stable speed.