Fuzzy decision support system knowledge base generation using a genetic algorithm

This paper presents a genetic algorithm (GA) that automatically constructs the knowledge base used by fuzzy decision support systems (FDSS). The GA produces an optimal approximation of a set of sampled data from a very small amount of input information. The main interest of this method is that it can be used to automatically generate (without the help of an expert) a fuzzy knowledge base ± i.e., the fuzzy sets for premises, conclusions and the fuzzy rules. This knowledge base is composed of the minimum number of fuzzy sets and rules. This minimalist approach produces fuzzy knowledge bases that are still manageable a posteriori by a human expert for ®ne tuning. The GA is validated through several examples of known behaviors and, ®nally, applied to experimental data.