On Generating Optimal Signal Probabilities for Random Tests: A Genetic Approach | Zendy

M. Srinivas | Zendy; L.M. Patnaik | Zendy

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On Generating Optimal Signal Probabilities for Random Tests: A Genetic Approach

Author(s) -

M. Srinivas,

L.M. Patnaik

Publication year - 1996

Publication title -

vlsi design

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.123

H-Index - 24

eISSN - 1065-514X

pISSN - 1026-7123

DOI - 10.1155/1996/75798

Subject(s) - benchmark (surveying) , genetic algorithm , gradient descent , testability , random search , convergence (economics) , mathematical optimization , algorithm , function (biology) , computer science , descent direction , mathematics , artificial intelligence , artificial neural network , statistics , geodesy , evolutionary biology , economic growth , economics , biology , geography

Genetic Algorithms are robust search and optimization techniques. A Genetic Algorithm based approach for determining the optimal input distributions for generating random test vectors is proposed in the paper. A cost function based on the COP testability measure for determining the efficacy of the input distributions is discussed. A brief overview of Genetic Algorithms (GAs) and the specific details of our implementation are described. Experimental results based on ISCAS-85 benchmark circuits are presented. The performance of our GAbased approach is compared with previous results. While the GA generates more efficient input distributions than the previous methods which are based on gradient descent search, the overheads of the GA in computing the input distributions are larger

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