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A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery

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Advances in Evolutionary Computing

Part of the book series: Natural Computing Series ((NCS))

Abstract

This chapter discusses the use of evolutionary algorithms, particularly genetic algorithms and genetic programming, in data mining and knowledge discovery. We focus on the data mining task of classification. In addition, we discuss some preprocessing and postprocessing steps of the knowledge discovery process, focusing on attribute selection and pruning of an ensemble of classifiers. We show how the requirements of data mining and knowledge discovery influence the design of evolutionary algorithms. In particular, we discuss how individual representation, genetic operators and fitness functions have to be adapted for extracting high-level knowledge from data.

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Author information

Authors and Affiliations

  1. Postgraduate Program in Computer Science, Pontificia Universidade Catolica Do Parana, Rua Imaculada Conceicao, 1155, Curitiba-PR, 80215-901, Brazil

    Alex A. Freitas

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  1. Alex A. Freitas
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Editors and Affiliations

  1. Machine Intelligence Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata, 700 035, India

    Ashish Ghosh

  2. Department of Management Information, Hannan University, 5-4-33 Amamhigigashi, Matsubara, Osaka, 580-8502, Japan

    Shigeyoshi Tsutsui

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Freitas, A.A. (2003). A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery. In: Ghosh, A., Tsutsui, S. (eds) Advances in Evolutionary Computing. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18965-4_33

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  • DOI: https://doi.org/10.1007/978-3-642-18965-4_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-62386-8

  • Online ISBN: 978-3-642-18965-4

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