Exploring optimal parameters for multiple fault diagnosis using the simple genetic algorithm

Abstract

Multiple Fault Diagnosis (MFD) is the process of determining the correct fault or faults that are responsible for a given set of symptoms. MFD problems are generally characterized by problem-spaces containing many local minima and maxima. We show that when using Genetic Algorithms to solve these kinds of problems, best results can be achieved with higher than "normal" mutation rates. Schemata theory is then used to analyze this data and show why this genetic operator would give these results.