A database driven memetic algorithm for fuzzy set optimization

Fuzzy logic provides a natural and precise way for humans to define and interact with systems. Optimizing a fuzzy inference system, however, presents some special challenges for the developer because of the imprecision that is inherent to fuzzy sets. This paper expands upon an earlier development of a fuzzy framework, adding components for dynamic self-optimization. What makes this approach unique is the use of relational database as a computational engine for the memetic algorithm and fitness function. The new architecture combines the power of fuzzy logic with the special properties of a relational database to create an efficient, flexible and self-optimizing combination. Database objects provide the fitness function, population sampling, gene crossover and mutation components allowing for superior batch processing and data mining potential. Results show the framework is able to improve the performance of a working configuration as well as fix a non-working configuration.