Application of simple genetic algorithms to sequential circuit test generation

In this work we investigate the effectiveness of genetic algorithms (GAs) in the test generation process. We use simple GAs to generate populations of candidate test vectors and to select the best vector to apply in each time frame. A sequential circuit fault simulator is used to evaluate the fitness of each candidate vector, allowing the test generator to be used for both combinational and sequential circuits. We experimented with various GA parameters, namely population size, number of generations, mutation rate, and selection and crossover schemes. For the ISCAS85 combinational benchmark circuits, 100% of testable faults were detected in six of the ten circuits used, and very compact test sets were generated. Good results were obtained for many of the ISCAS89 sequential benchmark circuits, and execution times were significantly lower than in a deterministic test generator in most cases