Improving the genetic algorithm in fuzzy cluster analysis for numerical data and its applications

This study proposes an automatic genetic algorithm in fuzzy cluster analysis for numerical data. In this algorithm, anew measure called the FB index is used as the objective function of the genetic algorithm. In addition, the algorithmnot only determines the appropriate number of groups but also improves the steps of traditional genetic algorithmas crossover, mutation and selection operators. The proposed algorithm is shown the step by step throughout thenumerical example, and can perform fast by the established Matlab procedure. The result from experiments show thesuperiority of the proposed algorithm when it overcomes the existing algorithms. Moreover, it has been applied inrecognizing the image data, and building the fuzzy time series model. These show the potential of this study for manyreal applications of the different fields.