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A Novel and Efficient Selection Method in Genetic Algorithm

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2015
Smit Anand, Nishat Afreen, Shama Yazdani

Smit Anand, Nishat Afreen and Shama Yazdani. Article: A Novel and Efficient Selection Method in Genetic Algorithm. International Journal of Computer Applications 129(15):7-12, November 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Smit Anand and Nishat Afreen and Shama Yazdani},
	title = {Article: A Novel and Efficient Selection Method in Genetic Algorithm},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {129},
	number = {15},
	pages = {7-12},
	month = {November},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


The performance of a Genetic Algorithm (GA) is inspired by a number of factors: the choice of the selection method the type of crossover operator, the rate of mutation, population size etc. GA allows a diverse population to evolve under a specific selection scheme to fitter population. Therefore, the choice of the selection method plays a very important role in the maximization of the fitness function of the evolved population. In this paper, a novel selection method called “Alternis” has been proposed. This study emphasizes on the comparison among the different selection methods used in GAs and the proposed method and evaluate their performance. Results of this study highlight the significant differences among the various selection schemes. The influence of the various selection methods on the performance of genetic algorithm can be estimated to assist the preference of a selection method. The aim of this paper is to propose a selection method which gives best overall performance in a widely diverse population.


  1. Baker, J. E.: Reducing Bias and Inefficiency in the Selection Algorithm. in [Grefenstette, J. J.: Proceedings of the Second International Conference on Genetic Algorithms and their Application, Hillsdale, New Jersey, USA: Lawrence Erlbaum Associates, 1987.], pp. 14-21, 1987.
  2. Goldberg, D. E., and Miller, B. L., “Genetic Algorithms, Tournament Selection, and the Effects of Noise”, Complex Systems, 9 (1995) 193- 212
  3. Kumar, R, and Jyotishree, “Blending Roulette Wheel Selection & Rank Selection in Genetic Algorithms”, International Journal of Machine Learning and Computing, Vol. 2, No. 4, August 2012
  4. J.E. Baker, Adaptive selection methods for genetic algorithms, in Proceedings of the First International Conference on Genetic Algorithms, Lawrence Erlballm Associates, Hillsdale, NJ, pp. 101-111, 1985Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
  5. Goldberg, D. E., and Deb, K., "A Comparative Analysis of Selection Schemes used in Genetic Algorithms," Foundations of Genetic Algorithms, 1 (1991) 69-93 (also TCGA Report 90007.
  6. Blickle, T, and Thiele L, “A Comparison of Selection Schemes used in Genetic Algorithms”, TIK-Report, Nr. 11, December 1995, Version 2 (2. Edition)
  7. Beasley D, Bull D. R. and Martin R.R, “An overview of Genetic Algorithms: Part 1, Fundamentals”, University Computing, 1993, 15(2) 58 69.
  8. O. A. Jadaan, L. Rajamani, C. R. Rao, “Improved Selection Operator for GA,” Journal of Theoretical and Applied Information Technology, 2005
  9. B. A. Julstrom, It‟s All the Same to Me: Revisiting Rank-Based Probabilities and Tournaments, Department of Computer Science, St. Cloud State University, 1999
  10. J. Zhong, X. Hu, M. Gu, J. Zhang, “Comparison of Performance between Different Selection Strategies on Simple Genetic Algorithms,” Proceeding of the International Conference on Computational Intelligence for Modelling, Control and automation, and International Conference of Intelligent Agents, Web Technologies and Internet Commerce, 2005.
  11. Handbook of Evolutionary Computation, IOP Publishing Ltd. and Oxford University Press, 1997
  12. K. S. Goh, A. Lim, B. Rodrigues, Sexual Selection for Genetic Algorithms, Artificial Intelligence Review 19: 123 – 152, Kluwer Academic Publishers, 2003
  13. J. H. Holland, Adaptation in natural and artificial systems, The University of Michigan press, 1975
  14. P.G. Bachhouse, A.F. Fotheringham, and G. Allan, A comparison of a genetic algorithm with an experimental design technique in the optimization of a production process, Journal of Operational Research Society, Vol. 48, pp. 247-254, 1997
  15. T., Selective pressure in evolutionary algorithms: a characterization of selection mechanisms, in Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE Press, Orlando, FL, pp. 57-62, 1994


Genetic algorithm, Chromosomes, Crossover, Mutation, Fitness function.