Analysis of Roulette Wheel Selection and Steady state Selection Using Genetic Algorithm Techniques

International Journal of Computer & Organization Trends  (IJCOT)          
© 2015 by IJCOT Journal
Volume - 5 Issue - 3
Year of Publication : 2015
AuthorsM.Mayilvaganan, Geethamani G.S


M.Mayilvaganan, Geethamani G.S"Analysis of Roulette Wheel Selection and Steady state Selection Using Genetic Algorithm Techniques", International Journal of Computer & organization Trends (IJCOT), V4(3):13-16 May - Jun 2015, ISSN:2249-2593, Published by Seventh Sense Research Group.

Abstract Genetic algorithm installation with a inhabitants of folks represented by chromosomes. Each chromosome is evaluated by its fitness value as computed by the intent function of the crisis. In genetic algorithms, the roulette wheel selection operator has spirit of utilization while steady state selection is influenced by exploration. In this paper, a analysis of these two selection operators is proposed that is a ideal merge of both i.e. searching and utilization. The population undergoes conversion using three primary genetic operators – selection, crossover and mutation which form new generation of population. The proposed solution is implemented in MATLAB using DNA Nucleotide Sequence of Cancer cells and the results were compared with roulette wheel selection and steady state selection with different problem sizes.


[1] J. Holland, Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor, 1975.
[2] D. E. Goldberg, Genetic algorithms in search, optimisation, and machine learning, Addison Wesley Longman, Inc., ISBN 0-201- 15767-5, 1989.
[3] P. Merz and B. Freisleben, ―Genetic Local Search for the TSP: New results, Proceedings of IEEE International Conference on Evolutionary Computation, IEEE Press, pp 159-164, 1977.
[4] S. Ray, S. Bandyopadhyay and S.K. Pal, ―Genetic operators for combinatorial optimization in TSP and microarray gene ordering, SpringerScience + Business Media, LLC, 2007.
[5] D. E. Goldberg and P. Segrest, ―Finite Markov chain analysis of genetic algorithms, Proceedings of the the Second International Conference
[6].L. Booker, Improving search in genetic algorithms, Genetic Algorithms and Simulated Annealing, Pitman, chapter 5, pp 61-73, 1987.
[7] D. Fogel , ―An introduction to simulated evolutionary optimization, IEEE Trans. Neural Networks 5 (1), pp 3-14, 1994.
[8] Dan Adler, ―Genetic Algorithms and Simulated Annealing: A Marriage Proposal, IEEE International Conference on Neural Networks 1993, San Francisco (CA), 1993.
[9] A. E. Eiben and,C.A.Schippers, On evolutionary exploration and exploitation, Fundamenta Informaticae, 35 IOS Press, pp 1-16, 1998.
[10] G. Van Dijck, M. M. Van Hulle and M.Wevers, ―Genetic Algorithm for Feature Subset Selection with Exploitation of Feature Correlations from Continuous Wavelet Transform: a real-case Application, International Journal of Information and Mathematical Sciences 1:4 2005 pp 233-237, 2005.
[11] O. Al jaddan, L. Rajamani and C. R. Rao, ―Improved Selection Operator for GA”, Journal of Theoretical and Applied Information Technology, pp 269–277, 2005.
[12] A. Tsenov, ―Simulated Annealing and Genetic Algorithm in Telecommunications Network Planning, International Journal of Information and Mathematical Sciences 2:4, pp 240-245, 2006.
[13] A. E. Eiben, M. E. Schut and A. R. de Wilde, ―Boosting Genetic Algorithms with Self-Adaptive Selection, IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, pp 1584-1589, 2006.
[14] Z. G. Wang, M. Rahman, Y. S. Wong and K.S.Neo, ―Development of Heterogeneous Parallel Genetic Simulated Annealing Using Multi-Niche Crowding, International Journal of Information and Mathematical Sciences 3:1, pp 55-62, 2007.
[15] S. B. Liu, K. M. Ng and H. L. Ong, ―A New Heuristic Algorithm for the Classical Symmetric Travelling Salesman Problem, International Journal of Computational and Mathematical Sciences, Volume 1, Number 4, pp 234- 238, 2007.

chromosome, roulette wheel, steady selection, crossover, mutation.