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Knowledge-Based Evolutionary Search for Inductive Concept Learning

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Knowledge Incorporation in Evolutionary Computation

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 167))

Summary

This chapter provides a short overview of a GA-based system for inductive concept learning (in a fragment of first-order logic) . The described system exploits problem—specific knowledge by means of ad-hoc selection, mutation operators, and optimization applied to the single individuals. We focus on the experimental analysis of selection operators incorporating problem knowledge.

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Author information

Authors and Affiliations

  1. Division of Mathematics and Computer Science, Faculty of Sciences, Vrije Universiteit, 1081HV, Amsterdam, The Netherlands

    Federico Divina & Elena Marchiori

Authors
  1. Federico Divina
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  2. Elena Marchiori
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Editor information

Editors and Affiliations

  1. Honda Research Institute Europe GmbH, Carl-Legien-Str.30, D-63073, Offenbach/Main, Germany

    Yaochu Jin

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© 2005 Springer-Verlag Berlin Heidelberg

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Divina, F., Marchiori, E. (2005). Knowledge-Based Evolutionary Search for Inductive Concept Learning. In: Jin, Y. (eds) Knowledge Incorporation in Evolutionary Computation. Studies in Fuzziness and Soft Computing, vol 167. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44511-1_12

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  • DOI: https://doi.org/10.1007/978-3-540-44511-1_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-06174-5

  • Online ISBN: 978-3-540-44511-1

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