Fuzzy modeling employing fuzzy polyploidy genetic algorithms

Abstract

Fuzzy modeling generally comprises structure identification and parameter identification. The former determines the structure of a rule-base, whereas the latter determines the contents of each rule. Applying neural networks or genetic algorithms to identify the parameter sets and structures of a fuzzy system is increasingly popular owing to their ability to learn and adapt. However, most conventional approaches cannot integrate structure identification and parameter identification efficiently. This work presents a general approach to fuzzy modeling, i.e. fuzzy polyploidy genetic algorithms which integrate structure identification and parameter identification in a single evolution process. Capable of simulating the structural adaptation process of natural evolution, the proposed model is a generalized model for simultaneously optimizing both structure and parameters of fuzzy rule-bases. The structural adaptation proposed herein provides complete structural operations to simulate structural variation process and simple to complex life form of natural evolution. Illustrative examples involving typical FLCs, such as Mamdani and TSK models, demonstrate the effectiveness of applying the polyploidy scheme.