### 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 |
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Country | India |

City | New Delhi |

Period | 1/03/17 → 3/03/17 |

### Fingerprint

### Cite this

*Proceedings of the 11th INDIACom*(pp. 1465-1469). New Delhi, India: BVICAM.

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*Proceedings of the 11th INDIACom.*BVICAM, New Delhi, India, pp. 1465-1469, 11th INDIACom, New Delhi, India, 1/03/17.

**Genetic Algorithms for the Travelling Salesman Problem : A Crossover Comparison.** / ALQURA'N ALZYADAT, Tariq; Yamin, Mohammad; CHETTY, Girija.

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 -