A Simulation-Based Test Generation Scheme Using Genetic Algorithms

This paper discusses a Genetic Algorithm-based method of generating test vectors for detecting faults in combinational circuits. The GA-based approach combines the merits of two techniques that have been used previously for generating test vectors - the directed search approach and the random test method. We employ a variant of the traditional GA, the Adaptive GA (AGA), to improve the effi cacy of the genetic search. Two cost functions that are used for assessing the quality of the vectors are discussed. The performance of the AGA-based test generation approach has been evaluated using ISCAS-85 benchmark circuits. In our approach, the number of vectors that need to be simulated for detecting all detectable faults is significantly smaller than that required for a random test method. Even when optimized input distributions are used to generate the random test vectors, the AGA sustains its superior performance over the random test method.

[1]  Prathima Agrawal,et al.  Probabilistic Analysis of Random Test Generation Method for Irredundant Combinational Logic Networks , 1975, IEEE Transactions on Computers.

[2]  Hans-Joachim Wunderlich.  PROTEST: A Tool for Probabilistic Testability Analysis , 1985, DAC 1985.

[3]  Hans-Joachim Wunderlich.  Multiple distributions for biased random test patterns , 1990, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[4]  Rajarshi Das,et al.  A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization , 1989, ICGA.

[5]  Lalit M. Patnaik,et al.  Adaptive probabilities of crossover and mutation in genetic algorithms , 1994, IEEE Trans. Syst. Man Cybern..

[6]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[7]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[8]  Vishwani D. Agrawal,et al.  A sequential circuit test generation using threshold-value simulation , 1988, [1988] The Eighteenth International Symposium on Fault-Tolerant Computing. Digest of Papers.

[9]  Franc Brglez,et al.  Testability-Driven Random Test-Pattern Generation , 1987, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[10]  Gilbert Syswerda,et al.  Uniform Crossover in Genetic Algorithms , 1989, ICGA.

[11]  Prathima Agrawal,et al.  A directed search method for test generation using a concurrent simulator , 1989, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[12]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[13]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[14]  Thomas J. Snethen.  Simulator-oriented fault test generator , 1988, 25 years of DAC.

[15]  Vishwani D. Agrawal,et al.  Unified Methods for VLSI Simulation and Test Generation , 1989 .

[16]  Eric Lindbloom,et al.  The Weighted Random Test-Pattern Generator , 1975, IEEE Transactions on Computers.