Precise Feature Selection in Predictive Genetic Models using Grey Wolf Optimization Algorithm

摘要

Genetic models are overarching ideas on how genes work in individuals to affect phenotypes. The process of feature selection is a vital process to be merged in the genetic model. Genetic models specifically relate genotype to phenotype. In lots of instances, the found facts are steady with those sorts of fashions and a few analyses of genetic records are in large part descriptive. The feature selection process in genetic models faces some difficulties arising from the correlation structures present in DNA structure data. An optimization algorithm is neededto solve these difficulties The main objective of this paper is to introduce an accurate feature selection process in genetic models. This aim is verified using an accurate and green optimization algorithm entitled grey Wolf Optimization algorithm GWO. The results depict that GWO solve the problem of the correlation structures in DNA sequence data more precise and efficiently with average efficiency equals 1.19E+02.