Advertisement

Implementation of Generative Crossover Operator in Genetic Algorithm to Solve Traveling Salesman Problem

  • Devasenathipathi N. Mudaliar
  • Nilesh K. Modi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 324)

Abstract

The research work aims to solve symmetric traveling salesman problem more efficiently. In this research paper, a different crossover operator is proposed, which produces 18 valid offsprings from two parents. The performance of proposed crossover operator is compared with three other existing crossover operators by maintaining the selection technique, mutation technique, and fitness function identical. This crossover operator is tested with data from TSP dataset. The intercity distance table of cities in which distance is measured with L1 norm formed the input to the coded C program that implemented the proposed crossover operator. The same dataset was used to compare the performance of this crossover operator with other three crossover operators. The comparative study indicates that proposed crossover operator performs well compared to other crossover operators in solving traveling salesman problem.

Keywords

Symmetric traveling salesman problem Multiple offspring producing crossover operator Performance of crossover operator Intercity distance table Fitness function 

References

  1. 1.
    R. Agarwala, D.L. Applegate, D. Maglott, G.D. Schuler, A.A. Schäffer, A fast and scalable radiation hybrid map construction and integration strategy. Genome Res. 10, 350–364 (2000)CrossRefGoogle Scholar
  2. 2.
    D.N. Mudaliar, N.K. Modi. Evolutionary algorithm approach to pupils’ pedantic accomplishment, in Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA), Advances in intelligent systems and computing, vol. 199 (Springer, Berlin, 2013), pp. 415–423Google Scholar
  3. 3.
    R. Matai, S.P. Singh, M.L. Mittal, in Traveling salesman problem: an overview of applications, formulations, and solution approaches, Traveling Salesman Problem, Theory and Applications (InTech, Croatia, 2010), pp. 1–24Google Scholar
  4. 4.
    N. Bansal, A. Blum, S. Chawla, A. Meyerson, Approximation Algorithms for Deadline-TSP and Vehicle Routing with Time-Windows, in Proceedings of ACM STOC (2004), pp. 166–174Google Scholar
  5. 5.
    M. Ünal, Ak. Ayça, V. Topuz, H. Erdal, Genetic algorithm optimization of PID controllers using ant colony and genetic algorithm, Studies in computational intelligence. vol. 449 (Springer Berlin, 2013), pp. 19–29Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • Devasenathipathi N. Mudaliar
    • 1
    • 2
  • Nilesh K. Modi
    • 3
  1. 1.MCA DepartmentSVITVasadIndia
  2. 2.R & D CentreBharathiar UniversityCoimbatoreIndia
  3. 3.MCA DepartmentSVICSKadiIndia

Personalised recommendations