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Using Non-uniform Crossover in Genetic Algorithm Methods to Speed up the Generation of Test Patterns for Sequential Circuits

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Methods and Applications of Artificial Intelligence (SETN 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2308))

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Abstract

Due to the high complexity of the problem of generating test patterns for digital circuits Genetic Algorithms (GA) have been investigated as an alternative to deterministic algorithms for test generation. In this paper a Genetic Algorithm “GATPG” is presented for generating sequences of test vectors for sequential circuits. The aim is to produce compact test sequences that attain high fault coverage. Because of the constraints imposed on a GA by the peculiar characteristics of sequential circuits it is proposed here a nonuniform selection probability for crossover combined with individuals (test sequences) of variable length and a two-phase fitness function. For the evaluation of candidate test sequences is used a 3-valued fault simulator, allowing the test patterns to be applied on faulty circuits that start from an arbitrary (unknown) state. Experimental results with respect to the ISCAS’89 benchmarks are presented to show the viability of the proposed approach.

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Authors and Affiliations

  1. Department of Informatics, Aristotle University of Thessaloniki, GR-54006, Thessaloniki, GREECE

    Michael Dimopoulos & Panagiotis Linardis

Authors
  1. Michael Dimopoulos
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  2. Panagiotis Linardis
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Editors and Affiliations

  1. Dept. of Informatics, Aristotle University of Thessaloniki, 54006, Thessaloniki, Greece

    Ioannis P. Vlahavas

  2. N.C.S.R. “Demokritos”, Inst. of Informatics & Telecommunications Software and Knowledge Engineering Lab, 15310, Aghia Paraskevi, Greece

    Constantine D. Spyropoulos

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© 2002 Springer-Verlag Berlin Heidelberg

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Dimopoulos, M., Linardis, P. (2002). Using Non-uniform Crossover in Genetic Algorithm Methods to Speed up the Generation of Test Patterns for Sequential Circuits. In: Vlahavas, I.P., Spyropoulos, C.D. (eds) Methods and Applications of Artificial Intelligence. SETN 2002. Lecture Notes in Computer Science(), vol 2308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46014-4_43

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  • DOI: https://doi.org/10.1007/3-540-46014-4_43

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43472-6

  • Online ISBN: 978-3-540-46014-5

  • eBook Packages: Springer Book Archive

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