An Analysis on Feature Selection Method using Real Coded Genetic Algorithm (RCGA)

Authors

  • Sonal Mishra Mishra
  • Anamika Ahirwar

DOI:

https://doi.org/10.37591/josettt.v5i1.1579

Abstract

The work presented in this thesis mainly focuses on the solving protein structure prediction (PSP) problem from machine learning models. The prediction of distance-to-native of protein structure is carried out using regression machine learning models with six physicochemical properties. The objective is to predict the distance to the native like structure in the absence of its true native state and report how far a structure is from its true native. In this work, the Real Coded Genetic Algorithm (RCGA) is used for feature selection. The major objective of this thesis is to develop web based applications that predict the distance-to-native of a protein structure in the absence of its true native state. This web application will help the researchers to determine the distance to the native of their protein structure.

Keywords: RCGA, protein structure prediction, machine learning model

Cite this Article
Sonal Mishra, Anamika Ahirwar. An Analysis on Feature Selection Method using Real Coded Genetic Algorithm (RCGA). Journal of Software EngineeringTools & Technology Trends. 2018; 5(1): 23–30p.

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Published

2018-07-10

Issue

Vol. 5 No. 1 (2018)

Section

Research Articles

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