Performance Evaluation of Reproduction Operators in Genetic Algorithm

  • Hari Mohan PandeyEmail author
  • Nidhi Jain
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 5)


The performance of a GA largely depends on its parameters: crossover, mutation and selection. There exist many crossover and mutation operators are proposed. The primary interest of this paper is to investigate the effectiveness of the various reproduction operators. The conceptual characteristics of the combination of reproduction operators in the context of Travelling Salesman Problem (TSP) are discussed. Extensive experiments are conducted to compare the performance of 3-crossovers and 3-mutation operators. The computational experiments are performed and the results are collected. Statistical tests are conducted that demonstrate the superiority of 2-point cut crossover and swap mutation operators combination.


Crossover Genetic algorithm Mutation Reproduction operators etc. 


  1. 1.
    Koenig, Andreas C. “A study of mutation methods for evolutionary algorithms.” University of Missouri-Rolla (2002).Google Scholar
  2. 2.
    Lin, Wen-Yang, Wen-Yung Lee, and Tzung-Pei Hong. “Adapting crossover and mutation rates in genetic algorithms.” J. Inf. Sci. Eng. 19.5 (2003): 889–903.Google Scholar
  3. 3.
    Srinivas, Mandavilli, and Lalit M. Patnaik. “Adaptive probabilities of crossover and mutation in genetic algorithms.” IEEE Transactions on Systems, Man, and Cybernetics 24.4 (1994): 656–667.Google Scholar
  4. 4.
    De Jong, Kenneth. “Adaptive system design: a genetic approach.” IEEE Transactions on Systems, Man, and Cybernetics 10.9 (1980): 566–574.Google Scholar
  5. 5.
    Kaya, Yılmaz, and Murat Uyar. “A novel crossover operator for genetic algorithms: ring crossover.” arXiv preprint arXiv:1105.0355 (2011).Google Scholar
  6. 6.
    Pandey, Hari Mohan, Ankit Chaudhary, and Deepti Mehrotra. “A comparative review of approaches to prevent premature convergence in GA.” Applied Soft Computing 24 (2014): 1047–1077.Google Scholar
  7. 7.
    Pandey, Hari Mohan, Anurag Dixit, and Deepti Mehrotra. “Genetic algorithms: concepts, issues and a case study of grammar induction.” Proceedings of the CUBE International Information Technology Conference. ACM, 2012.Google Scholar
  8. 8.
    Holland J. H. “Genetic algorithms.” Scientific American 267.1 (1992): 66–72.Google Scholar
  9. 9.
    Magalhaes-Mendes, Jorge. “A comparative study of crossover operators for genetic algorithms to solve the job shop scheduling problem.” WSEAS transactions on computers 12.4 (2013): 164–173.Google Scholar
  10. 10.
    Noraini, Mohd Razali, and John Geraghty. “Genetic algorithm performance with different selection strategies in solving TSP.” (2011).Google Scholar
  11. 11.
    Grefenstette, John, et al. “Genetic algorithms for the traveling salesman problem.” Proceedings of the first International Conference on Genetic Algorithms and their Applications. Lawrence Erlbaum, New Jersey (160–168), 1985.Google Scholar
  12. 12.
    Shukla, Anupriya, Hari Mohan Pandey, and Deepti Mehrotra. “Comparative review of selection techniques in genetic algorithm.” Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE), 2015 International Conference on. IEEE, 2015.Google Scholar
  13. 13.
    Pandey, Hari Mohan. “Performance Evaluation of Selection Methods of Genetic Algorithm and Network Security Concerns.” Procedia Computer Science 78 (2016): 13–18.Google Scholar
  14. 14.
    Pandey, Hari Mohan, et al. “Evaluation of Genetic Algorithm’s Selection Methods.” Information Systems Design and Intelligent Applications. Springer India, 2016. 731–738.Google Scholar
  15. 15.
    Pandey, Hari Mohan. “Parameters Quantification of Genetic Algorithm.” Information Systems Design and Intelligent Applications. Springer India, 2016. 711–719.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringAmity UniversityNoidaIndia

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