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An Efficient Grouping Genetic Algorithm

by R.Sivaraj, Dr.T.Ravichandran
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 21 - Number 7
Year of Publication: 2011
Authors: R.Sivaraj, Dr.T.Ravichandran
10.5120/2520-3424

R.Sivaraj, Dr.T.Ravichandran . An Efficient Grouping Genetic Algorithm. International Journal of Computer Applications. 21, 7 ( May 2011), 38-42. DOI=10.5120/2520-3424

@article{ 10.5120/2520-3424,
author = { R.Sivaraj, Dr.T.Ravichandran },
title = { An Efficient Grouping Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { May 2011 },
volume = { 21 },
number = { 7 },
month = { May },
year = { 2011 },
issn = { 0975-8887 },
pages = { 38-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume21/number7/2520-3424/ },
doi = { 10.5120/2520-3424 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:08:05.949446+05:30
%A R.Sivaraj
%A Dr.T.Ravichandran
%T An Efficient Grouping Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 21
%N 7
%P 38-42
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Genetic algorithm is an intelligent way for solving combinatorial, NP hard problems and many other problems which cannot be easily solved by applying traditional mathematical formula. The proposed method gives a new variant of the Standard Genetic algorithm which is very simple and will easily find the solution even for complex problems. It implements the concept of grouping to reach the optimal solution. The selection pressure which is a crucial factor that determines the efficiency of the algorithm is very much reduced in the proposed algorithm. The convergence velocity of the algorithm is greatly improved thereby reducing the time taken for the algorithm to reach the solution.

References
  1. Holland, J.H., Adaptation in Natural and Artificial Systems, 2nd Edition, MIT Press, 1992.
  2. Tanese, R, ‘Distributed Genetic algorithm’, Proceedings of the Third International Conference on Genetic Algorithms, 1989,pp. 434-439.
  3. Grosso, P.B., ‘Computer Simulations of Genetic Adaptation: Parallel Subcomponent Interaction in a Multilocus Model’, Doctoral Dissertation, the University of Michigan, 1985.
  4. Pettey, C, B., Leuze, M. R., & Grefenstette, J. J., ‘ A Parallel Genetic Algorithm’, In Proceedings of the SecondInternational Conference on Genetic Algorithms, 1987,pp.155-161
  5. Back, T, ‘Evolutionary Algorithms in Theory and Practice’, Oxford University Press, 1996.
  6. G. Rudolph, “Convergence analysis of canonical genetic algorithms, ”IEEE Trans. Neural Networks, vol. 5, no. 1, pp. 96–101, 1994.
  7. Goldberg, D.E. ‘Genetic Algorithms in Search, Optimization and Machine Learning’, Addison-Wesley, 2003.
  8. M. Gen, R. Cheng, “Genetic Algorithms and Engineering Optimization,” John Wiley & Sons, Inc., New York, 2000.
  9. J. N. Bhuyan, V. V. Raghavan, and V. K. Elayavalli, “Genetic Algorithm for clustering with an ordered representation,” in Proc. 4th Int. Conf. Genetic Algorithms. San Mateo, CA: Morgan Kaufman, 1991.
  10. D. R. Jones and M. A. Beltramo, “Solving partitioning problems with genetic algorithms,” in Proc. 4th Int. Conf.Genetic Algorithms. San Mateo, CA: Morgan Kaufman, 1991.
  11. G. P. Babu, “Connectionist and evolutionary approaches for pattern clustering,” Ph.D. dissertation, Dept. Comput. Sci. Automat., Indian Inst.Sci., Bangalore, Apr. 1994.
  12. Martello, S, Toth, P: ‘ Knapsack problems: Algorithms and Computer Implementations’, J.Wiley & Sons, 1990
Index Terms

Computer Science
Information Sciences

Keywords

Genetic algorithms Selection Pressure Convergence Velocity

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