Efficient comparison-based fault diagnosis of multiprocessor systems using an evolutionary approach

In comparison models for system-level fault diagnosis pairs of units are given the same job and results are compared. The result of such a comparison test can be 0 (match) or 1 (mismatch) and diagnosis is based on the collection of test results. Two such models have been studied, among others: the symmetric model of Chwa and Hakimi and the asymmetric model of Malek. In this paper a novel approach is proposed for identifying faulty units, based on a well-known optimization procedure, as genetic algorithms, which have proven to be useful in various kinds of problems. Furthermore, a new problem-specific genetic mutation is presented and shown to be better than the standard one. A series of simulations was conducted to show the efficiency of the genetic-based approach