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Reseach Article

Effect of Annealing Selection Operators in Genetic Algorithms on Benchmark Test Functions

by Rakesh Kumar, Jyotishree
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 40 - Number 3
Year of Publication: 2012
Authors: Rakesh Kumar, Jyotishree
10.5120/5027-7174

Rakesh Kumar, Jyotishree . Effect of Annealing Selection Operators in Genetic Algorithms on Benchmark Test Functions. International Journal of Computer Applications. 40, 3 ( February 2012), 38-46. DOI=10.5120/5027-7174

@article{ 10.5120/5027-7174,
author = { Rakesh Kumar, Jyotishree },
title = { Effect of Annealing Selection Operators in Genetic Algorithms on Benchmark Test Functions },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 40 },
number = { 3 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 38-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume40/number3/5027-7174/ },
doi = { 10.5120/5027-7174 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:27:08.757752+05:30
%A Rakesh Kumar
%A Jyotishree
%T Effect of Annealing Selection Operators in Genetic Algorithms on Benchmark Test Functions
%J International Journal of Computer Applications
%@ 0975-8887
%V 40
%N 3
%P 38-46
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The strategies to find optimal solutions can be broadly categorized into two: exploration and exploitation, but it has been shown in the literature that none can be claimed better than others in all the problems or all stages of the problems. In evolutionary approaches such as genetic algorithm, different operators used are inclined either towards exploration or exploitation but problems demand the operators having the blend of both. In this paper an annealed selection operator has been proposed, the behavior of which is controlled by the current generation i.e. in early cycle of evolution it is more like exploration and gradually it shifts towards exploitation. The experiments have been conducted using five different benchmark functions and implementation is carried out using MATLAB. Results show the improvement over existing selection operators.

References
  1. Holland J. 1975. Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor.
  2. Goldberg D.E. 1989. Genetic algorithms in search, optimisation, and machine learning. Addison Wesley Longman, Inc. ISBN 0-201-15767-5.
  3. Merz P. and Freisleben B. 1977. Genetic Local Search for the TSP: New results. In Proceedings of IEEE International Conference on Evolutionary Computation, IEEE Press, 159-164.
  4. Ray S., Bandyopadhyay S. and Pal S.K. 2007. Genetic operators for combinatorial optimization in TSP and microarray gene ordering. SpringerScience + Business Media, LLC.
  5. Kumar R. and Jyotishree. 2011. Blending roulette wheel selection & rank selection in genetic algorithms. In Proceedings of 3rd International conference on machine learning and computing, V4, IEEE catalog number CFP1127J-PRT, ISBN 978-1-4244-9252-7, 197-202.
  6. Goldberg D.E. and Segrest P. 1987. Finite Markov chain analysis of genetic algorithms. In Proceedings of the the Second International Conference on Genetic Algorithms. Lawrence Erlbaum Associates, 1-8.
  7. Booker L. 1987. Improving search in genetic algorithms. Genetic Algorithms and Simulated Annealing. Pitman, chapter 5, 61-73.
  8. Fogel D. 1994. An introduction to simulated evolutionary optimization, IEEE Trans. Neural Networks 5 (1), 3-14.
  9. Al jaddan O., Rajamani L. and Rao C.R. 2005. Improved Selection Operator for GA. Journal of Theoretical and Applied Information Technology, 269–277.
  10. Wang Z.G., Rahman M., Wong Y.S. and Neo K.S. 2007. Development of Heterogeneous Parallel Genetic Simulated Annealing Using Multi-Niche Crowding. International Journal of Information and Mathematical Sciences 3:1, 55-62.
  11. Liu S.B., Ng K.M. and Ong H.L. 2007. A New Heuristic Algorithm for the Classical Symmetric Travelling Salesman Problem. International Journal of Computational and Mathematical Sciences. Volume 1, Number 4, 234-238.
  12. Sa Angela A.R., Andrade A.O., Soares A.B. and Nasuto S.J. 2008. Exploration vs. Exploitation in Differential Evolution. Volume 11: In Proceedings of the AISB 2008 Symposium on Swarm Intelligence Algorithms and Applications, ISBN 1 902956 70 2, 57-63.
  13. Thamilselvan R. and Balasubramanie P. 2009. A Genetic Algorithm with a Tabu Search (GTA) for Travelling Salesman Problem. International Journal of Recent Trends in Engineering. Issue I, Vol I, 607 – 610.
  14. Elhaddad Y. and Sallabi O. 2010. A New Hybrid Genetic and Simulated Annealing Algorithm to Solve the Traveling Salesman Problem. In Proceedings of the World Congress on Engineering 2010. Vol. I, ISBN: 978-988-17012-9-9, ISSN: 2078-0958 (Print); ISSN: 2078-0966 (Online), 11-14.
  15. Golberg D.E. and Deb K. 1991. A comparative analysis of selection schemes used in genetic algorithms. Foundations of Genetic Algorithms. San Mateo, CA, Morgan Kaufmann, 69-93.
  16. Fogel D. 1995. Evolutionary Computation, IEEE Press.
  17. Mitchell M. 1996. An Introduction to genetic algorithms. Prentice Hall of India, New Delhi, ISBN-81-203-1358-5.
  18. De Jong K.A. 1975. An Analysis of the behavior of a class of genetic adaptive systems (Doctoral dissertation, University of Michigan). Dissertation Abstracts International 36(10), 5140B University Microfilms No. 76/9381.
  19. Baker J.E. 1985. Adaptive selection methods for genetic algorithms. In Proceedings of an International Conference on Genetic Algorithms and their applications, 101-111.
  20. Whitley D. 1989. The GENITOR algorithm and selection pressure: why rank-based allocation of reproductive trials is best. In Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufmann, 116-121.
  21. Back T. and Hoffmeister F. 1991. Extended Selection Mechanisms in Genetic Algorithms. ICGA4, 92-99.
  22. Digalakis J.G. and Margaritis K.G. 2002. An Experimental Study of Benchmarking Functions for Genetic Algorithms. International Journal of Computer Mathematics, 79:4, 403-416.
  23. Salomon R. 1996. Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions: A survey of some theoretical and practical aspects of genetic algorithms, Elsevier:BioSystems, 39, 263-278.
Index Terms

Computer Science
Information Sciences

Keywords

Benchmark functions genetic algorithm rank selection roulette wheel selection