Abstract
There are some studies that genetic programmings have been used for creations of discriminant functions. However, these studies treated tree structure and it was very difficult to understand the evolved tree structure. Furthermore, the amounts of calculation increased very much. In this paper, we propose a method for evolution of the discriminant function using genetic algorithms and examine its performance for various experiments. We assume that the discriminant function can be approximated by polynomial expressions that are the product sum of input variables. Terms of the function are searched by genetic algorithms and the coefficients of each term are calculated by the multiple regression analysis. From experimental results, we comfirmed that the proposed method was effective for the creation of the discriminant function.
