Using Immune Genetic Algorithm in ATPG

In this paper, an immune genetic based algorithm (IGA) for random test pattern generation was proposed. Genetic algorithms (GA) solve many search and optimization problems, effectively. However, they may drop into local optimal solutions; or they may find the optimal solution by low convergence speed. To overcome these problems, we used the immune concept and GA algorithm for random-based test generation. In the proposed algorithm, some of the main characteristics of the immune system were used to enhance the GA algorithm. As a result, a new random-based test pattern generation technique based on immune genetic algorithm (IGA) was presented. Experimental results showed that the proposed algorithm improved the ability of global search, avoided dropping into the local optimal solutions and increased the speed of computation convergence with respect to previously proposed non-immune GA algorithms. The proposed algorithm improved the test size with a factor of about 25 % in comparison with non-immune algorithms.