Genetic and Hybrid Gray Wolf Optimization Algorithm (Indian Journal of Forensic Medicine & Toxicology, 2020)
Much of the data in the classification issue contains a number of additional attributes that do not affectthe accuracy of the classification. There are many evolutionary algorithms that are used to define thefeature and reduce dimensional patterns such as the gray wolf algorithm (GWO) after converting it from acontinuous space to a discrete space. In this research, a method of feature selection was proposed throughtwo consecutive stages in the first stage, the mutual information (MI) method is used to determine the mostimportant feature selection. In the second stage, the binary gray wolf optimization (BGWO) algorithm isused to lessen and determine a specific number of features affecting the process of classification, whichcame from the first stage. The proposed algorithm MI_BGWO is efficient and effective by obtaining higherclassification accuracy and a small number of specific features compared to other competing algorithms.