Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 11th INDIACom |
| Editors | Dr. M.N. Hoda |
| Place of Publication | New Delhi, India |
| Publisher | BVICAM |
| Pages | 1465-1469 |
| Number of pages | 5 |
| ISBN (Electronic) | 9789380544243 |
| Publication status | Published - 8 Mar 2017 |
| Event | 11th INDIACom: 2017 4th International Conference on Computing for Sustainable Global Development - Bharati Vidyapeeth's Institute of Computer Applications and Management (BVICAM), New Delhi, India Duration: 1 Mar 2017 → 3 Mar 2017 Conference number: 40353 |
Conference
| Conference | 11th INDIACom |
|---|---|
| Country | India |
| City | New Delhi |
| Period | 1/03/17 → 3/03/17 |
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Genetic Algorithms for the Travelling Salesman Problem : A Crossover Comparison. / ALQURA'N ALZYADAT, Tariq; Yamin, Mohammad; CHETTY, Girija.
Proceedings of the 11th INDIACom. ed. / Dr. M.N. Hoda. New Delhi, India : BVICAM, 2017. p. 1465-1469.Research output: A Conference proceeding or a Chapter in Book › Conference contribution
TY - GEN
T1 - Genetic Algorithms for the Travelling Salesman Problem
T2 - A Crossover Comparison
AU - ALQURA'N ALZYADAT, Tariq
AU - Yamin, Mohammad
AU - CHETTY, Girija
PY - 2017/3/8
Y1 - 2017/3/8
N2 - This paper addresses the use of Genetic Algorithms (GA) in solving the Travelling Salesman Problem (TSP), it compares the results of implementing two different approaches of ‘Order Crossover 1’ genes crossover, the static and the random position 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 the use of Genetic Algorithms (GA) in solving the Travelling Salesman Problem (TSP), it compares the results of implementing two different approaches of ‘Order Crossover 1’ genes crossover, the static and the random position 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 - Machine Learning
KW - Travelling Salesman Problem
M3 - Conference contribution
SP - 1465
EP - 1469
BT - Proceedings of the 11th INDIACom
A2 - Hoda, Dr. M.N.
PB - BVICAM
CY - New Delhi, India
ER -