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Reseach Article

An Efficient Shuffled Frog Leaping Algorithm for Clustering of Gene Expression Data

Published on November 2014 by Athul Jose, M Pandi
International Conference on Innovations in Information, Embedded and Communication Systems
Foundation of Computer Science USA
ICIIECS - Number 1
November 2014
Authors: Athul Jose, M Pandi
57b43b9b-aaf1-4531-bdc4-a53cb8f7dc55

Athul Jose, M Pandi . An Efficient Shuffled Frog Leaping Algorithm for Clustering of Gene Expression Data. International Conference on Innovations in Information, Embedded and Communication Systems. ICIIECS, 1 (November 2014), 18-23.

@article{
author = { Athul Jose, M Pandi },
title = { An Efficient Shuffled Frog Leaping Algorithm for Clustering of Gene Expression Data },
journal = { International Conference on Innovations in Information, Embedded and Communication Systems },
issue_date = { November 2014 },
volume = { ICIIECS },
number = { 1 },
month = { November },
year = { 2014 },
issn = 0975-8887,
pages = { 18-23 },
numpages = 6,
url = { /proceedings/iciiecs/number1/18649-1432/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Innovations in Information, Embedded and Communication Systems
%A Athul Jose
%A M Pandi
%T An Efficient Shuffled Frog Leaping Algorithm for Clustering of Gene Expression Data
%J International Conference on Innovations in Information, Embedded and Communication Systems
%@ 0975-8887
%V ICIIECS
%N 1
%P 18-23
%D 2014
%I International Journal of Computer Applications
Abstract

Shuffled frog-leaping algorithm is an evolutionary algorithm, which uses a stochastic search method that mimics natural biological evolution and the social deeds of species. These evolutionary algorithms are developed to find a new optimum solution for optimization problems that cannot be solved by gradient based mathematical methods. The generation of frog leaping algorithm is drawn from two other search techniques: the local search of the particle swarm optimizationand the competitiveness mixing of information of the shuffled complex evolution technique. This paper then introduces a new parameter for acceleration of searching into the formulation of the original shuffled frog leaping algorithm to create a modified form of the algorithm for effective clustering of gene expression data.

References
  1. Eusuff M, Lansey K, Pasha F. 2006. Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. EngOptim 38(2):129–154.
  2. Eusuff MM, Lansey K. E. 2003. Optimizing of water distribution network design using the shuffled frog leaping algorithm. J Water Resour Plan Manage 129(3):210–225.
  3. Elbeltagi E, Hegazy T, Grierson D 2005 Comparison among five evolutionary-based optimization algorithms. J Adv Eng Inform 19:43–53.
  4. Shokri Z, Selim, Al-Sultan K. 1991. A simulated annealing algorithm for the clustering problem. Pattern Recognition 24 (10):1003–1008.
  5. Sung CS, Jin HW. 2000. A Tabu-search-based heuristic for clustering. Pattern Recognition 33:849–858.
  6. Shelokar PS, Jayaraman VK, Kulkarni BD . 2004. An ant colony approach for clustering. Anal ChimActa 509:187–195.
  7. Gungor Z, Unler A . 2007. K-harmonic means data clustering with simulated annealing heuristic. Appl Math Comput 184:199–209
  8. Lovbjerg, M. , 2002. Improving particle swarm optimization by hybridization of stochastic search heuristics and self-organized criticality. MSc thesis, Aarhus University, Denmark.
  9. X. H. Luo, Y. Yang, X. Li, 2009. Modified shuffled frog-leaping algorithm to solve traveling salesman problem, Journal of Communications 30 (7) 130–135.
  10. J. P. Luo, M. R. Chen, X. Li, . ICIEA2009. A novel hybrid algorithm for global optimization based on EO and SFLA, in: Proceedings of the Fourth IEEE conference on Industry electronics and applications, Xi'an, China, 2009, pp. 1935–1939.
  11. Emad E, Tarek H, Donald G. 2005. Comparison among five evolutionary-based optimization algorithms. Adv Eng Inform 19:43–53.
  12. Spath H, 1989. Clustering analysis algorithms. Ellis Horwood,Chichester, UK
  13. Kuo RI, Wang HS, Hu T-L, Chou SH. 2005. Application of ant K-means on clustering analysis. Comput Math Appl 50:1709–1724.
Index Terms

Computer Science
Information Sciences

Keywords

Memeplexes evolutionary Algorithms shuffling Memetic Evolution Swarm.