A Dependable SNMP-based Tool for Distributed Network Management
DSN '02 Proceedings of the 2002 International Conference on Dependable Systems and Networks
An Evolutionary Algorithm for Identifying Faults in t-Diagnosable Systems
SRDS '00 Proceedings of the 19th IEEE Symposium on Reliable Distributed Systems
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Distributed Diagnosis in Dynamic Fault Environments
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Evolutionary Computation
International Journal of Parallel, Emergent and Distributed Systems
A survey of comparison-based system-level diagnosis
ACM Computing Surveys (CSUR)
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The size and complexity of systems based on multiple processing units demand techniques for the automatic diagnosis of their state. System-level diagnosis consists in determining which units of a system are faulty and which are fault-free. Elhadef and Ayeb have proposed a specialized genetic algorithm (GA) that can be used to accomplish diagnosis. This work extends their approach, describing and comparing several evolutionary algorithms for system-level diagnosis. Implemented algorithms include a simple genetic algorithm, a specialized GA both with and without crossover and specialized versions of the compact GA and Population-Based Incremental Learning both with and without negative examples. These algorithms had their performance evaluated using four metrics: the average number of generations needed to find the solution, the average fitness after up to 500 generations, the percentage of tests that found the optimal solution and the average time until the solution was found. An analysis of experimental results shows that more sophisticated algorithms converge faster to the optimal solution.