Genetic Algorithms in Test Pattern Generation

In current thesis, two test pattern generation approaches based on genetic algorithms are presented. The, first algorithm is designed so that it allows direct comparison with random method. Comparative results show that genetic algorithm performs better on large circuits, in the last stage of test generation when much of search effort must be made in order to detect still undetected faults. Experimental results on ISCAS'85 benchmarks [6] also show that the proposed algorithm performs significantly better than similar genetic approach published in [18]. In addition, the test sets generated by the algorithm here are more compact. The second algorithm presented in current thesis was aimed to solve the difficult problem of sequential circuit testing. Finite state machine model representation of the sequential circuit was targeted. Implemented prototype program can work standalone or together with the test generator introduced in [12] [13]. In latter case, evolutionary program tries to detect faults, which had remained undetected. Preliminary experimental results were given and further improvements to program were discussed. järjestikskeemide testimise algoritmile.