Genetic tuning for improving wang and Mendel's fuzzy database

This paper aims to use the Genetic Tuning, an evolutionary approach to improving an existing fuzzy database of a fuzzy system, which the rule base is generated by example, through fitness function manipulation. Evolutionary Computing is shown efficient for solving problems where the solution space is too large and its complete analyses is computationally unfeasible. The evolutionary solution will be applied in the adjustment of fuzzy sets generated by the Wang and Mendel's method. Wang and Mendel's method high performance has been clearly demonstrated. Although, inducing genetic tuning, a better set of parameters of the database can be achieved. Our proposal is to find a better representation of the input data attributes and find a minimum distance between the original output of the data set and the output given by the Wang and Mendel's method keeping a good interpretability of the sets and improving the accuracy of the system. To analyze the performance we will use tree different data set, considering Real Coding Scheme to chromosome codification.