A Novel Approach to Classificatory Problem using Grammatical Evolution based Hybrid Algorithm

Numerous problems that where intelligent systems find application are classificatory in nature. These include Face Recognition, Speaker Recognition, Word Recognition, etc. In this paper we propose a new model for these classificatory problems. This model is based on the Grammatical Evolution which is an Evolutionary Algorithm that uses chromosomes as a set of instructions over a predefined grammar. The model that we propose here is a type of fuzzy inference system. Rules are in form of a collection of points representing every class. The separation between the unknown input and these representative points determines the degree of belongingness of the unknown input to the specific class being considered. Multiple contributions from same classes are simply added together. The training data set is used for the purpose of generating the initial set of configurations of this fuzzy model. The fuzzy functions are parameterized by adding fuzzy parameters, like any neuro-fuzzy model. These parameters are trained by a validation data set using a training algorithm. The performance of the system over training and validation data set serve as the fitness function. Variable mutation rate is applied. We tested the effectiveness of the algorithm over the picture learning problem and received better results than numerous commonly used algorithms.