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A self-adaptive migration model genetic algorithm for data mining applications

Srinivasa, KG and Venugopal, KR and Patnaik, LM (2007) A self-adaptive migration model genetic algorithm for data mining applications. In: Information Sciences, 117 (20). pp. 4295-4313.

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Data mining involves nontrivial process of extracting knowledge or patterns from large databases. Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining. In this paper we propose a Self-Adaptive Migration Model GA (SAMGA), where parameters of population size, the number of points of crossover and mutation rate for each population are adaptively fixed. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions and a set of actual classification datamining problems. Michigan style of classifier was used to build the classifier and the system was tested with machine learning databases of Pima Indian Diabetes database, Wisconsin Breast Cancer database and few others. The performance of our algorithm is better than others.

Item Type: Journal Article
Publication: Information Sciences
Publisher: Elsevier Science
Additional Information: Copyright of this article belongs to Elsevier Science.
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited: 05 Mar 2010 07:19
Last Modified: 23 Oct 2018 08:10
URI: http://eprints.iisc.ac.in/id/eprint/25958

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