PARAMETERS OPTIMIZE METHOD BASED ON GENETIC AND SIMULATED ANNEALING ALGORITHMS

This paper gives an initial study on the comparison between Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA). Firstly, a new algorithm is presented. This method combines Genetic Algorithm and Simulated Annealing Algorithm, and it can be used to optimize the three parameters α, β and γ. It involes the rules that are extracted from Fuzzy Extension Matrix (FEM). These parameters play an important part in the entire process of rule extraction based on FEM. Secondly, it provides some theoretical support to the direct selection of the parameter values through experiments. Lastly, five data sets from the UCI Machine Learning centers are employed in the study. Experimental results and discussions are given.