Abstract
We introduce an optimization of information granules (IG)-based fuzzy model with the aid of genetic algorithms (GAs) to describe projects in terms of complexity and development time in experimental software datasets. The proposed fuzzy model implements system structure and parameter identification with the aid of IG and GAs. To identify the structure and the parameters of fuzzy model we use genetic algorithms. The concept of information granulation was coped with to enhance the abilities of structural optimization of fuzzy model. Granulation of information realized with Hard C-Means clustering help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions in the premise part and the initial values of polynomial functions in the consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the GAs and the least square method. An aggregate objective function is constructed in order to strike a sound balance between the approximation and generalization capabilities of the fuzzy model. The experimental results include well-known software data such as medical imaging system (MIS).