FEATURE SELECTION USING BIO INSPIRED ALGORITHMS - Anand, Neeyati; Sehgal, Shallu; Agarwal, Manisha; Sehgal, Riya; Anand, Sanchit; Bashambu, Arun (International Journal of Psychosocial Rehabilitation, 2020)
Bio-inspired computing optimization algorithms are recently developed algorithms which are influenced by the biological progression of nature. These algorithms are proving to be better than the traditional machine learning algorithms as they can determine optimal solution of complex problems in the field of science. This paper presents the 10 recent bio-inspired algorithms as well as their diverse applications, especially in the medical field namely- Artificial Bee Colony (ABC) Algorithm, Fish Swarm Algorithm (FSA), Cat Swarm Optimization (CSO), Whale Optimization Algorithm (WOA), Artificial Algae Algorithm (AAA), Cuttlefish Algorithm (CFA), Bat Algorithm(BA), Grasshopper Optimization Algorithm(GOA), Ant Lion Optimization Algorithm(ALO) and Crow Search Algorithm (CSA). We are trying to analyze the ways through which they mimic evolutionary operators. Also, we study and analyse the results with the help of feature selection using bio-inspired algorithms. It is an essential process that is relevant for predictive analysis. It is considered to be the most important step before using machine learning algorithms. Crow Search algorithm is used for feature selection and gives an accuracy of 98% by using Support vector machine(SVM).