An Effective Immune Algorithm for Multiple-valued Logic Minimization Problems

This paper presents an algorithm based on an artificial immune system to minimize the multiple-valued logic (MVL) functions. In the proposed algorithm, the length of antibody (solution) is adaptive in order to reduce the computational complexity. The affinity function is designed to consist of two metrics (correctness and optimality) so that the average number of product terms in the solution can be efficiently minimized. Furthermore, both the proportional clone operator and random hypermutation carry out the affinity maturation. Simulation results based on a great amount of MVL functions demonstrate the efficiency of the proposed method.