Fuzzy clustering using genetic algorithms

The problem of pattern classification or clustering can be viewed as a search for a set of ellipsoids which enclose each of the clusters, presuming that, in general, clusters in the pattern space are ellipsoidal in shape. We consider fuzzy ellipsoids by assigning fuzzy membership values to patterns against each of the ellipsoids. These membership values can be defuzzified for assigning a class to the pattern. In this paper we examine the use of genetic algorithms in generating fuzzy ellipsoids for learning the separation of the classes. Our evaluation function drives the genetic search towards the smallest ellipsoid which maximizes the number of correctly classified examples, and minimizes the number of misclassified examples