Parameter optimization for a class of general TS fuzzy controllers via a new DNA-based genetic algorithm

We propose a new approach to DNA-based genetic algorithms (DNA-GA) to optimize the design parameters for a class of general Takagi-Sugeno fuzzy controllers. The optimized design parameters are the input fuzzy sets and the linear consequent of the rules. The DNA-GA uses virus DNA encoding method stemmed from the structure of the biological DNA. Specifically, we use the frameshift decoding method of the virus DNA to encode the design parameters of the fuzzy controllers. The genetic operators of the method are based on the DNA genetic operations. Another novel aspect of our method is the introduction of an adaptive mutation rate, which is tuned by using a fuzzy logic technique. Our encoding method can significantly shorten the code length of DNA chromosomes and is suitable for complex knowledge representation. In addition, the method allows for easy implementation of the genetic operations at the gene level in the DNA-GA. As a demonstration, we show how to implement the new method to optimize the design parameters of the TS fuzzy controller. Computer simulation indicates that the designed fuzzy controller is satisfactory in control of a nonlinear system.