PT Journal
AU Alkafaween, E
   Hassanat, A
   Tarawneh, S
TI Improving Initial Population for Genetic Algorithm using the Multi Linear Regression Based Technique (MLRBT)
SO Communications - Scientific Letters of the University of Zilina
PY 2021
BP E1
EP E10
VL 23
IS 1
DI 10.26552/com.C.2021.1.E1-E10
WP https://komunikacie.uniza.sk/artkey/csl-202101-0001.php
DE genetic algorithm; population seeding; TSP; multi linear regression
SN 13354205
AB 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.
ER