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Reseach Article

An Observational Analysis of Genetic Operators

by T. Geetha, K. Muthu Kumaran
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 63 - Number 18
Year of Publication: 2013
Authors: T. Geetha, K. Muthu Kumaran
10.5120/10567-5583

T. Geetha, K. Muthu Kumaran . An Observational Analysis of Genetic Operators. International Journal of Computer Applications. 63, 18 ( February 2013), 24-34. DOI=10.5120/10567-5583

@article{ 10.5120/10567-5583,
author = { T. Geetha, K. Muthu Kumaran },
title = { An Observational Analysis of Genetic Operators },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 63 },
number = { 18 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 24-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume63/number18/10567-5583/ },
doi = { 10.5120/10567-5583 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:14:41.401201+05:30
%A T. Geetha
%A K. Muthu Kumaran
%T An Observational Analysis of Genetic Operators
%J International Journal of Computer Applications
%@ 0975-8887
%V 63
%N 18
%P 24-34
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Genetic algorithm is a search heuristic that mimics the natural process of evolution and it generates solution to a very complex NP-Hard problems. Genetic algorithm belongs to the class of evolutionary algorithms (EA) and it generates solution by using nature inspired techniques like selection, crossover and mutation. The performance of the genetic algorithm is mainly depends on the genetic operators. Genetic operators have the capability to maintain the genetic diversity. This paper mainly describes the available selection mechanisms as well as the crossover and the mutation operators.

References
  1. Talip Kellego¨z a,*, Bilal Toklu b, John Wilson c, "Comparing efficiencies of genetic crossover operators for one machine total weighted tardiness problem", Applied Mathematics and Computation 199 (2008) 590–598.
  2. Chao-Hsien Chu 1, G. Premkumar *, Hsinghua Chou 2,"Digital data networks design using genetic algorithms", European Journal of Operational Research 127 (2000) 140±158.
  3. Kusum Deep, Manoj Thakur *,"A new crossover operator for real coded genetic algorithms", Applied Mathematics and Computation 188 (2007) 895–911.
  4. Mustafa Kaya," The effects of a new selection operator on the performance of a genetic algorithm",Applied Mathematics and Computation 217 (2011) 7669–7678.
  5. Yong Xu a,b,*, Shen-Chu Xu a, Bo-Xi Wua, " Traffic grooming in unidirectional WDM ring networks using genetic algorithms",Computer communications 25 (2002) 1185-1194.
  6. C. Garc?´a-Mart?´neza,*,M. Lozanob,F. Herrerab,D. Molinac,A. M. Sa´nchezd," Global and local real-coded genetic algorithms based on parent-centric crossover operators", European Journal of Operational Research 185 (2008) 1088–1113.
  7. Li-Ning Xing a,b,*, Ying-Wu Chen a, Ke-Wei Yang a," A novel mutation operator based on the immunity operation", European Journal of Operational Research 197 (2009) 830–833.
  8. A. W. M. Nga,*, B. J. C. Pererab, "Selection of genetic algorithm operators for river water quality model calibration", Engineering Applications of Artificial Intelligence 16 (2003) 529–541.
  9. RC Chakraborty, "Fundamentals of Genetic Algorithms".
  10. R. SIVARAJ, Dr. T. RAVICHANDRAN," A REVIEW OF SELECTION METHODS IN GENETIC ALGORITHM", R. Sivaraj et al. / International Journal of Engineering Science and Technology (IJEST).
  11. Y?lmaz KAYA1 , Murat UYAR2, Ramazan TEK?N3," A Novel Crossover Operator for Genetic Algorithms:Ring Crossover".
  12. Dr. Elgasim Elamin Elnima Ali," A Proposed Genetic Algorithm Selection Method".
  13. Tom V. Mathew," Genetic Algorithm".
  14. Tobias Blickle, Lothar Thiele," A Comparison of Selection Schemes used in Genetic Algorithms".
  15. 1Omar Al Jadaan, 2Lakishmi Rajamani, 3C. R. Rao," IMPROVED SELECTION OPERATOR FOR GA",Journal of Theoretical and Applied Information Technology © 2005 – 2008, JATIT.
  16. Yang Chen?,Jinglu Hu†,Kotaro irasawa‡, "GARS: An Improved Genetic Algorithm with Reserve Selection for Global Optimization".
  17. Khaled Rasheed," Guided Crossover:A New Operator for Genetic Algorithm Based optimization".
  18. Carlos Conceição António," A study on synergy of multiple crossover operators in a hierarchical genetic algorithm applied to structural optimization", Struct Multidisc Optim (2009) 38:117–135,DOI 10. 1007/s00158-008-0268-x.
  19. Andrew Lima , Brian Rodriguesb, Fei Xiaoc,"A Genetic Algorithm with Hill Climbing for the Bandwidth Minimization Problem".
  20. Sandeep Rajoria1, Carlos Soares2, Jorge Pinho de Sousa3, and Joydip Dhar4," Predicting the Outcome of Mutation in Genetic Algorithms".
  21. Wilhelm Erben,HTWG Konstanz, "Genetic Algorithms", wilhelm. erben@htwg-konstanz. de.
  22. Kamaljit Kaur, Amit Chhabra & Gurvinder Singh," Heuristics Based Genetic Algorithm for Scheduling Static Tasks in Homogeneous Parallel System", International Journal of Computer Science and Security (IJCSS), Volume (4): Issue (2) 183.
  23. Dr. Masoud Yaghini," Genetic Algorithms Part 3: The Component of Genetic Algorithms", Spring 2009.
  24. Dr. Masoud Yaghini," Chapter 3: Genetic Algorithms", Spring 2009.
  25. "Tutorial: Genetic Algorithm"
  26. Muhammad Tami Al-Hajri, M. A. Abido," Assessment of Genetic Algorithm Selection,Crossover and Mutation Techniques in ReactivePower Optimization", 978-1-4244-2959-2/09/$25. 00_c 2009 IEEE.
  27. Shigeyoshi Tsutsui, Masayuki Yamamura, Takahide Higuchi,"Multi-parent Recombination with Simplex Crossover in Real Coded Genetic Algorithms", Ministry of Education, Science, Sports and Culture of Japan under Grantin-Aid for Scientific Research number 10680396.
  28. A. E. Eiben, C. H. M. van Kemenade, "Performance on Multi-Parent Crossover Operators on Numerical Function Optimization Problems".
  29. Yang Wang, Zhipeng L¨u_, and Jin-Kao Hao," A Study of Multi-parent Crossover Operators in a Memetic Algorithm" R. Schaefer et al. (Eds. ): PPSN XI, Part I, LNCS 6238, pp. 556–565, 2010. @Springer-Verlag Berlin Heidelberg 2010.
Index Terms

Computer Science
Information Sciences

Keywords

Genetic algorithm Selection mechanism Crossover Mutation