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Genetic Algorithm for Optimization of Multiple Objectives in Knowledge Discovery from Large Databases

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Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases

Part of the book series: Studies in Computational Intelligence ((SCI,volume 98))

Knowledge discovery in databases (KDD) is increasingly being accepted as a viable tool for collecting, analyzing, and making decision from massive data sets. Though many sophisticated techniques are developed by various interdisciplinary fields, only few of them are well equipped to handle multi-criteria issues of KDD. It seems to provide a new frontier of research directions. The KDD issues like feature selection, instance selection, rule mining and clustering involves simultaneous optimization of several (possibly conflicting) objectives. Further, considering a single criterion, as with the existing soft computing techniques like evolutionary algorithms (EA), neural network (NN), and particle swarm optimization (PSO) are not up to the mark. Therefore, the KDD issues have attracted considerable attention of the well established multi-objective genetic algorithms to optimize the identifiable objectives in KDD.

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Author information

Authors and Affiliations

  1. Machine Intelligence Unit, Indian Statistical Institute, 203 B. T. Road, 700 108, Kolkata, India

    Satchidananda Dehuri

  2. Department of Information and Communication Technology, F. M. University, 756 019, Balasore, India

    Susmita Ghosh

  3. Department of Computer Science and Engineering, Jadavpur University, 700 032, Kolkata, India

    Ashish Ghosh

Authors
  1. Satchidananda Dehuri
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  2. Susmita Ghosh
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  3. Ashish Ghosh
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Editor information

Editors and Affiliations

  1. Indian Statistical Institute, 203 B. T. Road, 700 108, Kolkata, India

    Ashish Ghosh

  2. F. M. University, 756 019, Balasore, India

    Satchidananda Dehuri

  3. Jadavpur University, 700 032, Kolkata, India

    Susmita Ghosh

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© 2008 Springer-Verlag Berlin Heidelberg

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Dehuri, S., Ghosh, S., Ghosh, A. (2008). Genetic Algorithm for Optimization of Multiple Objectives in Knowledge Discovery from Large Databases. In: Ghosh, A., Dehuri, S., Ghosh, S. (eds) Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases. Studies in Computational Intelligence, vol 98. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77467-9_1

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  • DOI: https://doi.org/10.1007/978-3-540-77467-9_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77466-2

  • Online ISBN: 978-3-540-77467-9

  • eBook Packages: EngineeringEngineering (R0)

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