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Self Adaptive Genetic Algorithms

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Soft Computing for Data Mining Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 190))

Abstract

Genetic Algorithms(GAs) are efficient and robust searching and optimization methods that are used in data mining. In this chapter, 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 chapter 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 based datamining problems. Michigan style of classifier is used to build the classifier and the system is tested with machine learning databases of Pima Indian Diabetes database, Wisconsin Breast Cancer database and few others.

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Authors
  1. K. R. Venugopal
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  2. K. G. Srinivasa
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  3. L. M. Patnaik
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Venugopal, K.R., Srinivasa, K.G., Patnaik, L.M. (2009). Self Adaptive Genetic Algorithms. In: Soft Computing for Data Mining Applications. Studies in Computational Intelligence, vol 190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00193-2_2

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  • DOI: https://doi.org/10.1007/978-3-642-00193-2_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00192-5

  • Online ISBN: 978-3-642-00193-2

  • eBook Packages: EngineeringEngineering (R0)

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