Total views : 58

Effect of Variations in the Population Size and Generations of Genetic Algorithms in Cryptography - An Empirical Study


  • Department of Computer Science, Kristu Jayanti College, Bangalore – 560077, Karnataka, India
  • Department of Computer Science and Engineering, M. S. Engineering College, Bangalore – 562110, Karnataka, India


Objectives: The implementation of Genetic algorithm in the symmetric block cipher Advanced Encryption Standard -128 (AES-128) algorithms to enhance the performance of cryptographic operations. Methods: Genetic algorithm is used for generating the best fit non-repetitive cipher key and for key distribution to design a dynamic Substitution box in AES-128. Findings: The study reveals that the efficiency of the cryptographic algorithm treated with Genetic algorithm is dependent on the variations in the number of generations and initial population size. The result shows that an optimum population size has less encryption and decryption time. Among the sample population size taken for the experiment, almost the average population size has minimum encryption and decryption time. Results from iteration variations shows that the average number of iterations has less encryption and decryption time. Improvements: The hybrid combination of Genetic algorithm and AES-128 can be further modified for images and audio messages also.


Genetic Algorithm, Iterations, Non-Repetitive Cipher Key, Population Size, Substitution Box.

Full Text:

 |  (PDF views: 24)


  • Stallings W. Cryptography and network security. Principles and Practice. 3rd ed. Prentice Hall; 2007.
  • Kalaiselvi K, Kumar A. Enhanced AES Cryptosystem by using Genetic Algorithm and Neural Network in S-box; 2016p. 1–3.
  • Goldberg DE. Genetic algorithms in search, optimization and machine learning. New York: Addison Wisley; 1989. p. 1–6.
  • Kapoor V, Dey S, Khurana AP. An empirical study of the role of control parameters of genetic algorithms in function optimization problems. International Journal of Computer Applications. 2011Oct; 31(6):20–6.
  • Goldberg DE, Deb K. A comparative analysis of selection schemes used in genetic algorithmIn: Rawlins, Gregory JE, Editor. Foundations of Genetic Algorithms. Morgan Kaufmann Publishers Inc; 1991. p. 69–93.
  • Tragha A, Omary FA, Mouloudi. ICIGA. Improved Cryptography Inspired By Genetic Algorithms. 2006, 3(2), pp. 1-3.
  • Batina L, Jakobovic D, Mentens N, Picek S, Piedra AD, Sisejkovic D. S-box pipelining using genetic algorithms for high-throughput AES implementations. 2014; 8885:322– 37.
  • Sindhuja K, Devi PS. A symmetric key encryption technique using genetic algorithm. International Journal of Computer Science and Information Technologies. 2014; 5(1):414–6.
  • Arrag S, Hamdoun A, Tragha A, Khamlich SE. Replace AES key expansion algorithm by modified genetic algorithm. Applied Mathematical Sciences. 2013; 7(144):7161–71. Crossref
  • Eiben AE, Hinterding R, Michalewicz Z. Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation.1999; 3(2):124–41. Crossref
  • Boyabalti O, Sabuncuoglu I. Parameter selection in genetic algorithms. Systemics, Cybernetics and Informatics. 2007; 2(4):78–83.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.