Structural and parametric design of fuzzy inference systems using hierarchical fair competition-based parallel genetic algorithms and information granulation

In this paper, we develop a design methodology for information granulation-based genetically optimized fuzzy inference system, which deals with the tuning method with a variant identification ratio for structural as well as parametric optimization of the reasoning system. The tuning is carried out with the aid of the hierarchical fair competition-based parallel genetic algorithms and it employs the mechanism of information granulation. This version of the genetic algorithm is a multi-population variant of parallel genetic algorithms, which is particularly suitable for handling multimodal problems of high-dimensionality. The granulation of information is realized with the aid of the C-Means clustering algorithm. The concept of information granulation is applied to the formation of the fuzzy inference system in order to realize its structural optimization. Here we divide the input space in order to construct the premise part of the fuzzy rules. Subsequently the consequence part of each fuzzy rule is organized based on the center points (prototypes) of data group obtained as a result of clustering. In particular, this concerns the fuzzy inference system-related parameters, i.e., the number of input variables to be used in the fuzzy inference system, a collection of a specific subset of input variables, the number of membership functions used for each input variable, and the polynomial type (order) occurring at the consequence part of fuzzy rules. Making use of a mechanism of simultaneous tuning for the parameters, we construct an optimized fuzzy inference system related to its structural as well as parametric optimization. A comparative analysis demonstrates that the proposed methodology leads to improved results when compared with some conventional methods exploited in fuzzy modeling.