We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
Reseach Article

Self-Organizing Genetic Algorithm: A Survey

by Amouda Nizam, Buvaneswari Shanmugham
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 65 - Number 18
Year of Publication: 2013
Authors: Amouda Nizam, Buvaneswari Shanmugham
10.5120/11025-5659

Amouda Nizam, Buvaneswari Shanmugham . Self-Organizing Genetic Algorithm: A Survey. International Journal of Computer Applications. 65, 18 ( March 2013), 25-32. DOI=10.5120/11025-5659

@article{ 10.5120/11025-5659,
author = { Amouda Nizam, Buvaneswari Shanmugham },
title = { Self-Organizing Genetic Algorithm: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 65 },
number = { 18 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 25-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume65/number18/11025-5659/ },
doi = { 10.5120/11025-5659 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:19:12.284027+05:30
%A Amouda Nizam
%A Buvaneswari Shanmugham
%T Self-Organizing Genetic Algorithm: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 65
%N 18
%P 25-32
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Self-organization systems are an increasingly attractive dynamic processes without a central control, emerge global order from local interactions in a bottom up approach. The advantage of blending the concept of self-organization enhances the working efficiency of other techniques to find a solution of huge search problem. Genetic Algorithms (GA) is such a technique, inspired by the natural evolution process, used to solve difficult optimization problem of large space solution, for an example, multiple sequence alignment (MSA) problem in a bioinformatics research. Self-organization technique automates the selection of appropriate parameter values of GA during execution without the user's intervention. An attempt towards applying Self-organizing Genetic Algorithm (SOGA) on MSA requires a complete knowledge of the various parameters of SO and its relationships. This lead us to make a complete survey on inherent properties of SO and the method of blending GA in order to develop a self-organizing genetic algorithm (SOGA) for MSA. The aim of the research is to make use of the efficiency of GA without getting any input from the non-trained users to tune the parameters in order to achieve the expected result.

References
  1. Camazine, S. , Deneubourg, J. L. , Franks, N. R. , Sneyd, J. , Theraulaz, J. G. , Bonabeau, E. 2001. Self-Organization in Biological Systems. Princeton University Press: New Jersey.
  2. Heylighen, F. 2002. The Science of Self-organization and Adaptivity. In: Knowledge Management, Organizational Intelligence and Learning, and Complexity; L. D. Kie, Eds. In: The Encyclopedia of Life Support Systems (EOLSS). Eolss Publishers: Oxford.
  3. Seeley, T. D. 2002. When Is Self-Organization Used in Biological Systems?. Biol. Bull. 202(3), 314–18.
  4. Jain, L. C. , Karr, C. L. 2000. Introduction to evolutionary computing technique. In: Proceedings of the Electronic Technology Directions to the Year 2000. 1995 May 23-25. Adelaide. SA, 122-27.
  5. Marczyk, A. 2004. Genetic Algorithms and Evolutionary Computation. The TalkOrigins Archive. 23 Apr. 2004. http://www. talkorigins. org/faqs/genalg/genalg. html
  6. Wu, S. , Lee, M. , Gatton, T. M. Multiple Sequence Alignment using GA and NN. International Journal of Signal Processing, Image Processing and Pattern Recognition: 21-30.
  7. Hong, T. , Wang, H. , Lin, W. , Lee, W. 2002. Evolution of Appropriate Crossover and Mutation Operators in a Genetic Process. Applied Intelligence. 16, 7-17.
  8. Zhang, J. , Zhuang, J. , Du, H. , Wang S. 2009. Self-organizing genetic algorithm based tuning of PID controllers. Information Sciences. 179 (7), 1007-18.
  9. Breukelaar, R. , Bäck, T. 2008. Self-Adaptive Mutation Rates in Genetic Algorithm for Inverse Design of Cellular Automata (July 2008), 12-6.
  10. Thierens, D. 2002. Adaptive mutation rate control schemes in genetic algorithms. Institute of Information and Computing Sciences. Utrecht Univerisity. The Netherlands.
  11. Bao-Juan, H. , Jian, Z. , De-Hong, Y. 2008. A Novel and Accelerated Genetic algorithm. WSEAS Transactions on Systems and Control. 3(4), 269-78.
  12. Kubota, N. , Fukuda, T. , Shimojima, K. 1996. Virus-evolutionary genetic algorithm for a self-organizing manufacturing system. Computers and Industrial Engineering. 30(4), 1015-26.
  13. Ray, S. S. , Bandyopadhyay, S. , Pal, S. K. 2005. New Genetic Operators for Solving TSP: Application to Microarray Gene Ordering. Springer-Verlag Berlin Heidelberg, 605–610.
  14. Soper, R. , Taylor, D. J. , Green, N. P. O. , Stout, G. W. 1997. Biological Science. 3rd ed. Cambridge University Press: United Kingdom.
  15. Introduction to genetic algorithm. www. obitko. com/tutorials/genetic-algorithms
  16. Genetic Server/ Library product: Neuro Dimension inc. www. nd. com/products/genetic
  17. Evolutionary Algorithm. http://www. geatbx. com/docu/algindex-02. html
  18. Rocha, L. M. Modeling evolution: evolutionary computation. Lecture notes, Biologically Inspired Computing. School of Informatics. Indiana University. http://informatics. indiana. edu/rocha/i-bic/
  19. Harik, G. R. , Lobo, F. G. 1999. A Parameter-Less Genetic Algorithm. IEEE Transactions on Evolutionary Computation, 523-8.
  20. Sivanandam, S. N. , Deepa, S. N. 2008. Introduction to Genetic Algorithms. Springer: New York.
  21. Kelso, J. A. S. 1995. Dynamic patterns: the self-organization of brain and behavior. MIT Press: USA.
  22. Fuchs, C. 2008. Internet and society: social theory in the information age. Routledge. New York.
  23. Notredame, C. 2002. Recent progresses in multiple sequence alignment: a survey. Pharmacogenomics. 3(1), 131-144.
  24. Thompson, J. D. , Higgins, D. G. , Gibson, T. J. 1994. CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting position specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673-80. http://www. ebi. ac. uk/Tools/clustalw/
  25. Corpet, F. 1988. Multiple sequence alignment with hierarchical clustering. Nucleic Acids Res. 16, 10881-90. http://bioinfo. genotoul. fr/multalin/multalin. html
  26. Morgenstern, B. , Dress, A. , Wener, T. 1996. Multiple DNA and protein sequence based on segment-to-segment comparison. Proc. Natl. Acad. Sci. 93, 12098-103. http://bibiserv. techfak. uni-bielefeld. de/dialign/
  27. Edgar, R. C. 2004. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792-7. http://www. ebi. ac. uk/Tools/muscle/
  28. Notredame, C. , Higgins, D. G. , Heringa, J. 2000. T-Coffee: A novel method for fast and accurate multiple sequence alignment. J Mol Biol. 302, 205-17. http://www. ebi. ac. uk/Tools/t-coffee/
  29. Stoye, J. , Moulton, V. , Dress, A. W. 1997. DCA: an efficient implementation of the divide-and conquer approach to simultaneous multiple sequence alignment. Comput. Appl. Biosci. 13(6), 625-6. http://bibiserv. techfak. uni-bielefeld. de/dca/
  30. Notredame, C. , Higgins, D. G. 1996. SAGA: sequence alignment by Genetic algorithm. Nucleic Acids Res. 24(8), 1515-24.
  31. Gondro, C. , Kinghorn, B. P. 2007. A simple Genetic Algorithm for multiple sequence alignment. Genet. Mol. Res. 6 (4), 964-82.
  32. Karadimitriou, K. , Kraft, D. H. 1996. Genetic Algorithms and the Multiple Sequence Alignment Problem in Biology. In Proc. 2nd Annual Molecular Biology and Biotechnology Conference, Baton Rouge, LA.
  33. Amouda, V. , Selvaraj, V. , Kuppuswami, S. , Buvaneswari, S. 2010. iMAGA: Intron Multiple Alignment Using Genetic Algorithm. International Journal of Engineering Science and Technology. 2(11), 6361-6370.
  34. Amouda, N. , Buvaneswari, S. , Kuppuswami, S. 2011. Self-Organizing Genetic Algorithm for Multiple Sequence Alignment. Global Journal of Computer Science and Technology. 11(7), 7-14.
  35. Amouda, V. , Buvaneswari, S. , Kuppuswami, S. 2011. Self organizing algorithm for multiple sequence alignment. Online Journal of Bioinformatics. 12(1), 74-84.
  36. Khayat, O. , Ebadzadeh, M. M. , Shahdoosti, H. R. , Rajaei R. , Khajehnasiri, I. 2009. A novel hybrid algorithm for creating self-organizing fuzzy neural networks. Neurocomputing. 73, 517-524.
  37. Deep, K. , Dipti. 2008. A self-organizing migrating genetic algorithm for constrained optimization. Applied Mathematics and Computation. 198, 237-250.
  38. Tinos, R. , Yang, S. 2007. A self-Organizing Random Immigrants Genetic Algorithm for Dynamic Optimization Problems. Genetic Programming and Evolvable Machines. 8(3), 255-286.
  39. Jeong, I. K. , Lee J. J. 1998. A self-organizing genetic algorithm for multimodal function optimization. Artif. Life Robotics. 2, 48-52.
Index Terms

Computer Science
Information Sciences

Keywords

Crossover Mutation Selection Self-organizing genetic algorithm