A Novel Genetic Grey Wolf optimizer for Global optimization and Feature Selection

In this paper, a new stochastic search strategy inspired by the Grey Wolf optimization theory is proposed for feature subset selection. Grey Wolf optimization algorithm (GWO) is a new metaheuristic optimization technique. Its principle is to reproduce the behavior of grey wolves in nature to hunt in a cooperative way. In this work, we have used the Grey Wolf optimizer and Genetic algorithm to select the most relevant features in a dataset. Then we have proposed a new Genetic Grey Wolf optimization algorithm. In our proposed strategy, feature selection algorithm is formulated as an optimization problem that searches an optimum with less number of features in a feature space and a good accuracy. The goal of our study is to achieve a balance between the classification accuracy and the size of the feature subsets selected. Our proposed approach has been evaluated on 10 standard datasets taken from UCI repository and validated on 02 big datasets used in literature. The experimental results show the superiority of GWO algorithm in classification performance and dimensionality reduction.