Fuzzy genetic algorithm approach to feature selection problem

Abstract

The primary role of a genetic algorithm is in the selective breeding of a population of individuals. A suboptimal solution can be obtained by such an algorithm which applies the paradigm of various evolutionary selection and searches through many generations via different genetic operators. The selection of features associated with each individual is based on the fitness-proportionate selection in which parents are chosen from the population. This fitness is a problem-specific property that describes an individual's performance upon some chosen features quantitatively. This paper describes a formal fuzzy genetic algorithm to overcome the traditional problems in feature classification and selection and provides fuzzy templates for the identification of the smallest subset of features. Simulation results demonstrate that the operation using soft crossover significantly improves the searching power through the multi-dimensional feature space.