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Applying Evolutionary Clustering Technique for finding the most Significant Solution from the Large Result Set obtained in Multi-Objective Evolutionary Algorithms

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IJCA Proceedings on National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2012)
© 2012 by IJCA Journal
ncipet - Number 14
Year of Publication: 2012
Authors:
P.M.Chaudhari
R.V. Dharaskar
V. M. Thakare

P.M.Chaudhari, R V Dharaskar and V M Thakare. Article: Applying Evolutionary Clustering Technique for finding the most Significant Solution from the Large Result Set obtained in Multi-Objective Evolutionary Algorithms. IJCA Proceedings on National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2012) ncipet(14):17-22, March 2012. Full text available. BibTeX

@article{key:article,
	author = {P.M.Chaudhari and R.V. Dharaskar and V. M. Thakare},
	title = {Article: Applying Evolutionary Clustering Technique for finding the most Significant Solution from the Large Result Set obtained in Multi-Objective Evolutionary Algorithms},
	journal = {IJCA Proceedings on National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2012)},
	year = {2012},
	volume = {ncipet},
	number = {14},
	pages = {17-22},
	month = {March},
	note = {Full text available}
}

Abstract

Multicriteria optimization applications can be implemented using Pareto optimization techniques including evolutionary Multicriteria optimization algorithms. Many real world applications involve multiple objective functions and the Pareto front may contain a very large number of points. Choosing a solution from such a large set is potentially intractable for a decision maker. Previous approaches to this problem aimed to find a representative subset of the solution set. Clustering techniques can be used to organize and classify the solutions. A Evolutionary algorithm-based k-means clustering technique is proposed in this paper. The searching capability of Evolutionary algorithms is exploited in order to search for appropriate cluster centres in the feature space such that a similarity metric of the resulting clusters is optimized. The chromosomes, which are represented as strings of real numbers, encode the centres of a fixed number of clusters. Applicability of this methodology for various applications and in a decision support system is also discussed.

References

  • 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.