Efficient Comparison-Based Fault Diagnosis of Multiprocessor Systems Using Genetic Algorithms
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
A comparison of evolutionary algorithms for system-level diagnosis
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
International Journal of Parallel, Emergent and Distributed Systems
A survey of comparison-based system-level diagnosis
ACM Computing Surveys (CSUR)
| Hi-index | 0.00 |
This paper describes a novel approach to the problem of system-level fault diagnosis using genetic algorithms. Consider a system composed of n independent units, each of which tests a subset of the others. It is assumed that at most t of these units is permanently faulty. Such a system is said to be t-diagnosable if, given any complete collection of test results, the set of faulty units can be uniquely identified. Genetic algorithms have recently received much attention as a class of robust stochastic search algorithms for various optimization problems. An efficient method based on evolutionary algorithms is developed to solve the diagnosis problem. The representation of the search space used is in the form of a binary vector of length n. Each bit indicates the status (faulty or fault-free) of its corresponding unit. Genetic operators are adapted to the context of system-level diagnosis. The genetic algorithm was implemented and tested on random test graphs. The simulation results demonstrate the efficiency of the proposed diagnosis algorithm.