On random pattern generation with the selfish gene algorithm for testing digital sequential circuits
Junwu Zhang, M.L. Bushnell, Vishwani D. Agrawal · 2005
A selfish gene (SG) algorithm differs from the genetic algorithm (GA) because it evolves genes (characteristics) that provide higher fitness rather than evolving individuals with higher fitness. We enhance the spectral method of sequential circuit test generation by using a SG algorithm. The objects of evolution are the Hadamard spectral matrix, non-linear digital signal processing (DSP) filtering cutoff values, vector holding time, and relative input phase shifts, which are all modeled as genes. These characteristics, extracted from compacted test vectors, are used to create new vector sequences to be further compacted with higher fault coverage. Alternatively, new vectors were generated by holding randomly selected vectors and then randomly perturbing some bits in 8-bit chunks of bit streams. Both the SG algorithm and holding with bit-perturbation can outperform the previously-published spectral method in either fault coverage, or shorter vector length, or both. The SG algorithm is often superior to random bit-perturbation but it requires more CPU time.