A Comprehensive Study on the Application of Grey Wolf Optimization for Microarray Data
Swati Sucharita, Barnali Sahu, Tripti Swarnkar · 2021
The new research trend is to achieve improved elucidation efficiency in the area of global optimization for various functional and real-life applications. Various nature inspired computational algorithms are now a promising approach to discovering new and robust methods, grounded on the theory of biological evolution of nature. The current survey focuses on a recent emerging optimization algorithm i.e. Grey Wolf Optimization (GWO) in various health care domains, and specifically in the Microarray data application. There is various nature inspired algorithms present in the literature and GWO is a recent developed algorithm. It has been applied in different engineering fields and successfully achieved the objectives. In the current research we have given a systematic review on the application of GWO in health care domain and gene selection in microarray data. Gene selection in health care is an impending arena that attains huge importance for extracting useful genes from the thousands of genes in microarray dataset. The gene selection plays a vital role in large datasets to increase the efficiency of classification to derive the important genes for high dimensional classification. The study also focuses on the variations of GWO and its application in medical field as well as in medical data classification and gene selection.