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Issue No. 01 - January (2010 vol. 59)
ISSN: 0018-9340
pp: 116-126
Fei He , Tsinghua University, Beijing
Xiaoyu Song , Portland State University, Portland
William N.N. Hung , Synopsys Inc., Mountain View
Ming Gu , Tsinghua University, Beijing
Jiaguang Sun , Tsinghua University, Beijing
Model checking for large-scale systems is extremely difficult due to the state explosion problem. Creating useful abstractions for model checking task is a challenging problem, often involving many iterations of refinement. In this paper we consider techniques for model checking in the counterexample-guided abstraction refinement. The state separation problem is one popular approach in counterexample-guided abstraction refinement, and it poses the main hurdle during the refinement process. To achieve effective minimization of the separation set, we present a novel probabilistic learning approach based on the sample learning technique, evolutionary algorithm, and effective heuristics. We integrate it with the abstraction refinement framework in the VIS [1] model checker. We include experimental results on model checking to compare our new approach to recently published techniques. The benchmark results show that our approach has overall speedup of more than 56 percent against previous techniques. Our work is the first successful integration of evolutionary algorithm and abstraction refinement for model checking.
Formal models, verification.

J. Sun, F. He, M. Gu, W. N. Hung and X. Song, "Integrating Evolutionary Computation with Abstraction Refinement for Model Checking," in IEEE Transactions on Computers, vol. 59, no. , pp. 116-126, 2009.
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