Improving Initial Population for Genetic Algorithm using the Multi Linear Regression Based Technique (MLRBT)

https://doi.org/10.26552/com.C.2021.1.E1-E10

  • Esra'a Alkafaween
  • Ahmad B. A. Hassanat
  • Sakher Tarawneh
Keywords: genetic algorithm, population seeding, TSP, multi linear regression

Abstract

Genetic algorithms (GAs) are powerful heuristic search techniques that are used successfully to solve problems for many different applications. Seeding the initial population is considered as the first step of the GAs.

In this work, a new method is proposed, for the initial population seeding called the Multi Linear Regression Based Technique (MLRBT). That method divides a given large scale TSP problem into smaller sub-problems and the technique works frequently until the sub-problem size is very small, four cities or less. Experiments were carried out using the well-known Travelling Salesman Problem (TSP) instances and they showed promising results in improving the GAs' performance to solve the TSP.

Author Biographies

Esra'a Alkafaween

IT Department, Mutah University, Karak, Jordan

Ahmad B. A. Hassanat

IT Department, Mutah University, Karak, Jordan and Computer Science Department, Community College, University of Tabuk, Tabuk, Saudi Arabia and Industrial Innovation and Robotics Center, University of Tabuk, Tabuk, Saudi Arabia

Sakher Tarawneh

Industrial Innovation and Robotics Center, University of Tabuk, Tabuk, Saudi Arabia

Published
2021-01-04
How to Cite
Esra’a Alkafaween, Ahmad B. A. Hassanat, & Sakher Tarawneh. (2021). Improving Initial Population for Genetic Algorithm using the Multi Linear Regression Based Technique (MLRBT). Communications - Scientific Letters of the University of Zilina, 23(1), E1-E10. https://doi.org/10.26552/com.C.2021.1.E1-E10
Section
Management Science and Informatics in Transport