Optimizing genetic algorithm parameters for multiple fault diagnosis applications

Multiple fault diagnosis (MFD) is the process of determining the correct fault or faults that are responsible for a given set of symptoms. Exhaustive searches or statistical analyses are usually too computationally expensive to solve these types of problems in real-time. We use a simple genetic algorithm to significantly reduce the time required to evolve a satisfactory solution. We show that when using genetic algorithms to solve these kinds of applications, best results are achieved with higher than “normal” mutation rates. Schemata theory is used to analyze this data and show that even though schema length increases, the Hamming distance between binary representations of best-fit chromosomes is quite small. Hamming distance is then related to schema length to show why mutation rate becomes important in this type of application