Genetic algorithms for the travelling salesman problem: a crossover comparison

  • Tariq Alzyadat
  • Mohammad YaminEmail author
  • Girija Chetty
Original Research


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.


Dynamic crossover Genetic algorithms Permutation Static crossover Travelling salesman problem 



Analysis of variance


The cycle crossover


Edge recombination crossover


Genetic algorithms


Generalized N-point crossover


Nondeterministic polynomial time problem


Sequential constructive crossover


Travelling salesman problem



We thank the Statistical consultant Mr. Julio Romero for his assistance in the statistical analysis of the data in this experiment.


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Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2019

Authors and Affiliations

  1. 1.University of CanberraCanberraAustralia
  2. 2.Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddahSaudi Arabia

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