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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.

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