Genetic and Hybrid Gray Wolf Optimization Algorithm
DOI:
https://doi.org/10.37506/ijfmt.v14i3.10838Keywords:
Gray wolf optimization; classification; feature selection; mutual information.Abstract
Much of the data in the classification issue contains a number of additional attributes that do not affect
the accuracy of the classification. There are many evolutionary algorithms that are used to define the
feature and reduce dimensional patterns such as the gray wolf algorithm (GWO) after converting it from a
continuous space to a discrete space. In this research, a method of feature selection was proposed through
two consecutive stages in the first stage, the mutual information (MI) method is used to determine the most
important feature selection. In the second stage, the binary gray wolf optimization (BGWO) algorithm is
used to lessen and determine a specific number of features affecting the process of classification, which
came from the first stage. The proposed algorithm MI_BGWO is efficient and effective by obtaining higher
classification accuracy and a small number of specific features compared to other competing algorithms.
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