Study of Differential Evolution on ATPG

As a new heuristic approach, differential evolution has shown superior performance in continuous space capable of handling nondifferentiable, nonlinear and multimodal objective functions. This paper reports a study of DE to operate on discrete binary variables, which are test patterns of sequential circuits. Preliminary experimental results of automatic test pattern generation (ATPG) for sequential circuits based on DE are provided and comparisons are discussed

[1]  René Thomsen,et al.  A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[2]  Elizabeth M. Rudnick,et al.  Sequential Circuit Test Generation in a Genetic Algorithm Framework , 1994, 31st Design Automation Conference.

[3]  Dimitris K. Tasoulis,et al.  Parallel differential evolution , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[4]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[5]  Yu Jian-kun.  Genetic Algorithm and Its Application , 2002 .

[6]  Dynamic state traversal for sequential circuit test generation , 2000, TODE.

[7]  Dimitris K. Tasoulis,et al.  Vector evaluated differential evolution for multiobjective optimization , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).