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Genetic algorithms for the travelling salesman problem: a crossover comparison

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

Abstract

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.

Keywords

Dynamic crossover Genetic algorithms Permutation Static crossover Travelling salesman problem 

Abbreviations

ANOVA

Analysis of variance

CX

The cycle crossover

ERX

Edge recombination crossover

GA

Genetic algorithms

GNX

Generalized N-point crossover

NP-problem

Nondeterministic polynomial time problem

SCX

Sequential constructive crossover

TSP

Travelling salesman problem

Notes

Acknowledgements

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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