A new DNA-based evolutionary algorithm with application to the design of fuzzy controllers

A new DNA-based evolutionary algorithm (DNA-EA) for automatic design of a class of Takagi-Sugeno (TS) fuzzy controllers is discussed in this paper. The fuzzy rules are automatically discovered, and the design parameters in the input fuzzy sets and the linear rule consequent are optimized simultaneously by the DNA-EA. The DNA-EA uses the DNA encoding method stemmed from the structure of the biological DNA to encode the design parameters of the fuzzy controllers. The gene transfer operation and bacterial mutation operation inspired by a microbial evolution phenomenon are introduced into the DNA-EA. Moreover, frameshift mutation operations based on the DNA genetic operations are used in the DNA-EA. Our encoding method is suitable for complex knowledge representation, and is easy for the genetic operations at gene level to be introduced into the DNA-EA. The length of the chromosome is variable and it is easy to insert and delete parts of the chromosome. As a demonstration, we show how to implement the new method to design automatically a TS fuzzy controller in the control of a nonlinear system. The fuzzy controller can be automatically constructed by the DNA-EA. Computer simulation results indicate that the new method is effective and the designed fuzzy controller is satisfactory