Automatic test pattern generation with estimation of distribution algorithms for combinational circuits

The fault coverages achieved by the test generation procedures based on genetic algorithms are smaller than the deterministic test generation procedures for combinational circuits. One of the possible causes for this deficiency is the high, long-distance schema, which exists in the process of automatic test pattern generation. Thus genetic algorithms in dealing with such problems easily fall into local optima or premature convergence. In this work, we firstly propose the test generation procedures based on estimation of distribution algorithms. Estimation of distribution algorithms are able to capture the interrelations between the primary inputs by joint probability distribution. And therefore obviate the influence of the high, long-distance schema; alleviate the problem of premature convergence. The experimental results for benchmark circuits prove that the proposed procedure can achieve higher fault coverage.