Call for Paper - August 2018 Edition
IJCA solicits original research papers for the August 2018 Edition. Last date of manuscript submission is July 20, 2018. Read More

Evolutionary Clustering Technique for finding Significant Solutions

IJCA Proceedings on National Conference on Recent Trends in Computing
© 2012 by IJCA Journal
NCRTC - Number 8
Year of Publication: 2012
P. M. Chaudhari
R. V. Dharaskar
V. M. Thakare

P M Chaudhari, R V Dharaskar and V M Thakare. Article: Evolutionary Clustering Technique for finding Significant Solutions. IJCA Proceedings on National Conference on Recent Trends in Computing NCRTC(8):19-23, May 2012. Full text available. BibTeX

	author = {P. M. Chaudhari and R. V. Dharaskar and V. M. Thakare},
	title = {Article: Evolutionary Clustering Technique for finding Significant Solutions},
	journal = {IJCA Proceedings on National Conference on Recent Trends in Computing},
	year = {2012},
	volume = {NCRTC},
	number = {8},
	pages = {19-23},
	month = {May},
	note = {Full text available}


Evolutionary clustering technique is proposed that opts for cluster centers straight way from the data set, further making it to speed up the fitness evaluation by estimating a data table in advance. It saves the distances among pairs of data points, and by using binary instead of string representation to encode a variable number of cluster centers. The development of ECT has capability to properly cluster different data sets. The experimental results show that the ECT provides a more stable clustering performance in terms of number of clusters and clustering results. These results require less computational time as compared to other GA-based clustering algorithms.


  • Zitzler E, Laumanns M, Thiele L. SPEA2: improving the strength Pareto evolutionary algorithm. Swiss Federal Institute Techonology: Zurich, Switzerland; 2001.
  • Rosenman, M. A. and J. S. Gero. 1985. Reducing the pareto optimal set in multicriteria optimization (with applications to pareto optimal dynamic programming). Engineering Optimization, 8, 189–206.
  • Kata Praditwong and Xin Yao. How Well Do Multi-objective Evolutionary Algorithms Scale to Large Problems. 2007 IEEE Congress on Evolutionary Computation (CEC 2007)
  • M. Laumanns, L. Thiele, E. Zitzler, and K. Deb. Archiving with guaranteed convergence and diversity in multi-objective optimization. In W. B. Langdon, E. Cant´u-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M. A. Potter, A. C. Schultz, J. F. Miller, E. Burke, and N. Jonoska, editors, GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 439–447, New York, 9-13 July 2002. Morgan Kaufmann Publishers.
  • A. Mosavi. Multiple Criteria Decision-Making Preprocessing Using Data Mining Tools. IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 2, No 1, March 2010 ISSN (Online): 1694-0784 ISSN (Print): 1694-0814
  • Lily Rachmawati, and Dipti Srinivasan, Senior Member, IEEE. MulticriteriaEvolutionary Algorithm with Controllable Focus on the Knees of the Pareto Front. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 13, NO. 4, AUGUST 2009
  • Helmuth Spaeth. Cluster Analysis Algorithms. John Wiley and Sons, 1980.
  • Cherhan Foo and Michael Kirley. An analysis of the effects of clustering in graph-based evolutionary Algorithms. 2008 IEEE Congress on Evolutionary Computation (CEC 2008)
  • Kiri Wagsta , Claire Cardie, Seth Rogers, Stefan Schroedl. Constrained K-means Clustering with Background Knowledge. Proceedings of the Eighteenth International Conference on Machine Learning, 2001, p. 577-584.
  • Yiu-Ming Cheung. k_-Means: A new generalized k-means clustering algorithm. Pattern Recognition Letters 24 (2003) 2883–2893.
  • Pradyumn Kumar Shukla and Kalyanmoy Deb. On Finding Multiple Pareto-Optimal Solutions Using Classical and Evolutionary Generating Methods. KanGAL Report Number 2005006
  • Dilip Datta, Kalyanmoy Deb and Carlos M. Fonseca. Solving Class Timetabling Problem of IIT Kanpur using Multi-Objective Evolutionary Algorithm. KanGAL Report Number 2006006
  • Eckart Zitzler, Marco Laumanns, and Lothar Thiele. SPEA2: Improving the Strength Pareto Evolutionary Algorithm. TIK-Report 103,May 2001
  • Maiyaporn Phanich, Phathrajarin Pholkul, and Suphakant Phimoltares. Food Recommendation System Using Clustering Analysis for Diabetic Patients. Advanced Virtual and Intelligent Computing (AVIC) Research Center
  • Jun Zhang, Member, IEEE, Henry Shu-Hung Chung, Senior Member, IEEE, and Wai-Lun Lo, Member, IEEE. Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 11, NO. 3, JUNE 2007
  • P. M. Chaudhari, R. V. Dharaskar , V. M. Thakare , "Computing the Most Significant Solution from Pareto Front obtained in Multi-objective Evolutionary Algorithms", International Journal of Advanced Computer Science and Applications (IJACSA 2010), Vol. 1(4), pp. 63-68