Formation of Regression Model for Analysis of Complex Systems Using Methodology of Genetic Algorithms

TL;DR

This paper introduces a genetic algorithm-based method for constructing non-linear regression models to analyze and predict the evolution of complex systems across various domains, including education.

Contribution

It presents a novel theoretical approach using genetic algorithms to identify regression models that relate factors in complex systems, regardless of their nature.

Findings

Regression models enable prediction of system evolution

Factors' significance can be determined from models

Applicable to educational data analysis

Abstract

This study presents the approach to analyzing the evolution of an arbitrary complex system whose behavior is characterized by a set of different time-dependent factors. The key requirement for these factors is only that they must contain an information about the system; it does not matter at all what the nature (physical, biological, social, economic, etc.) of a complex system is. Within the framework of the presented theoretical approach, the problem of searching for non-linear regression models that express the relationship between these factors for a complex system under study is solved. It will be shown that this problem can be solved using the methodology of \emph{genetic (evolutionary)} algorithms. The resulting regression models make it possible to predict the most probable evolution of the considered system, as well as to determine the significance of some factors and, thereby, to formulate some recommendations to drive by this system. It will be shown that the presented theoretical approach can be used to analyze the data (information) characterizing the educational process in the discipline "Physics" in the secondary school, and to develop the strategies for improving academic performance in this discipline.