A GENETIC ALGORITHM FRAMEWORK FOR TEST-GENERATION

Test generation using deterministic fault-oriented algorithms is highl y complex and time consuming, New approaches are needed to augment the existing techniques, both to reduce execution time and to improve fau lt coverage, Genetic algorithms (GA's) have been effective in solving many search and optimization problems, Since test generation is a sear ch process over a large vector space, it is an ideal candidate for GA' s. In this work, we describe a GA framework for sequential circuit tes t generation, The GA evolves candidate test vectors and sequences, usi ng a fault simulator to compute the fitness of each candidate test, Va rious GA parameters are studied, including alphabet size, fitness func tion, generation gap, population size, and mutation rate, as web as se lection and crossover schemes, High fault coverages were obtained for most of the ISCAS'89 sequential benchmark circuits, and execution time s were significantly lower than in a deterministic test generator in m ost cases.