Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Koenig, Andreas C. “A study of mutation methods for evolutionary algorithms.” University of Missouri-Rolla (2002).
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.
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.
De Jong, Kenneth. “Adaptive system design: a genetic approach.” IEEE Transactions on Systems, Man, and Cybernetics 10.9 (1980): 566–574.
Kaya, Yılmaz, and Murat Uyar. “A novel crossover operator for genetic algorithms: ring crossover.” arXiv preprint arXiv:1105.0355 (2011).
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.
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.
Holland J. H. “Genetic algorithms.” Scientific American 267.1 (1992): 66–72.
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.
Noraini, Mohd Razali, and John Geraghty. “Genetic algorithm performance with different selection strategies in solving TSP.” (2011).
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.
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.
Pandey, Hari Mohan. “Performance Evaluation of Selection Methods of Genetic Algorithm and Network Security Concerns.” Procedia Computer Science 78 (2016): 13–18.
Pandey, Hari Mohan, et al. “Evaluation of Genetic Algorithm’s Selection Methods.” Information Systems Design and Intelligent Applications. Springer India, 2016. 731–738.
Pandey, Hari Mohan. “Parameters Quantification of Genetic Algorithm.” Information Systems Design and Intelligent Applications. Springer India, 2016. 711–719.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Pandey, H.M., Jain, N. (2017). Performance Evaluation of Reproduction Operators in Genetic Algorithm. In: Satapathy, S., Bhateja, V., Raju, K., Janakiramaiah, B. (eds) Computer Communication, Networking and Internet Security. Lecture Notes in Networks and Systems, vol 5. Springer, Singapore. https://doi.org/10.1007/978-981-10-3226-4_46
Download citation
DOI: https://doi.org/10.1007/978-981-10-3226-4_46
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3225-7
Online ISBN: 978-981-10-3226-4
eBook Packages: EngineeringEngineering (R0)