IBitABC: Improved Binary Artificial Bee Colony Algorithm with Local Search

Abstract

Feature selection is a process of selecting a subset of features that is highly distinguishable from the data set to obtain better or at least equivalent success rates. Artificial Bee Colony (ABC) Algorithm is a intelligence algorithm that model the behavior of honey bees in the nature of food seeking behavior and has been developed to produce a solution at continuous space. BitABC is a bitwise operator based binary ABC algorithm that can produce fast results in binary space. In this study, BitABC was improved to increase the local search capacity and adapted to the feature selection problem to measure the success of the proposed method. The results obtained using 10 data sets from UCI Machine Learning Repository indicate the success of the proposed method.