TY - JOUR
T1 - Genetic algorithms for the travelling salesman problem: a crossover comparison
AU - Alqura'N Alzyadat, Tariq
AU - Yamin, Mohammad
AU - Chetty, Girija
PY - 2020/3
Y1 - 2020/3
N2 - This paper addresses an application of genetic algorithms (GA) for solving the travelling salesman problem (TSP), it compares the results of implementing two different types of two-point (1 order) genes crossover, the static and the dynamic approaches, which are used to produce new offspring. By changing three factors; the number of cities, the number of generations and the population size, the goal is to show which approach is better in terms of finding the optimal solution (the shortest path) in as short time as possible as a result of these changes. Besides, it will explore the effect of changing the above factors on finding the optimal solution.
AB - This paper addresses an application of genetic algorithms (GA) for solving the travelling salesman problem (TSP), it compares the results of implementing two different types of two-point (1 order) genes crossover, the static and the dynamic approaches, which are used to produce new offspring. By changing three factors; the number of cities, the number of generations and the population size, the goal is to show which approach is better in terms of finding the optimal solution (the shortest path) in as short time as possible as a result of these changes. Besides, it will explore the effect of changing the above factors on finding the optimal solution.
KW - Genetic algorithms
KW - Travelling Salesman Problem
KW - Optimisation
UR - https://www.mendeley.com/catalogue/e9cecfd2-f987-3fe3-9681-522f1df4c235/
U2 - 10.1007/s41870-019-00377-9
DO - 10.1007/s41870-019-00377-9
M3 - Article
VL - 12
SP - 209
EP - 213
JO - International Journal of Information Technology
JF - International Journal of Information Technology
SN - 2511-2112
IS - 1
ER -