Hybrid Monkey Algorithm with Krill Herd Algorithm Optimization for Feature Selection

In this work, a system for feature selection based on hybrid Monkey Algorithm (MA) with Krill Herd Algorithm (KHA) is proposed. Data sets ordinarily includes a huge number of attributes, with irrelevant and redundant attribute. A system for feature selection is proposed in this work using a hybrid Monkey Algorithm and Krill Herd Algorithm (MAKHA). The MAKHA algorithm adaptively balance the exploration and exploitation to quickly find the optimal solution. MAKHA is a new evolutionary computation technique, inspired by the chicken movement. The MAKHA can quickly search the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The proposed fitness function used incorporate both classification accuracy and feature reduction size. The proposed system was tested on 18 data sets and proves advance over other search methods as particle swarm optimization (PSO) and genetic algorithm (GA) optimizers commonly used in this context using different evaluation indicators.