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.
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