Advertisement

An Evolutionary Approach to Concept Learning with Structured Data

  • Claire J. Kennedy
  • Christophe Giraud-Carrier

Abstract

This paper details the implementation of a strongly-typed evolutionary programming system (STEPS) and its application to concept learning from highly-structured examples. STEPS evolves concept descriptions in the form of program trees. Predictive accuracy is used as the fitness function to be optimised through genetic operations. Empirical results with representative applications demonstrate promise.

Keywords

Genetic Programming Crossover Point Concept Learning Inductive Logic Inductive Logic Programming 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    C. Giraud-Carrier A.F. Bowers and J.W. Lloyd. Higher-order logic for knowledge representation in inductive learning. In preparation, 1999.Google Scholar
  2. [2]
    M. Bongard. Pattern Recognition. Spartan Books, 1970.Google Scholar
  3. [3]
    P. Flach, C. Giraud-Carrier, and J.W. Lloyd. Strongly typed inductive concept learning. In Proceedings of the International Conference on Inductive Logic Programming (ILP′98), pages 185–194,1998.Google Scholar
  4. [4]
    C.J. Kennedy. Evolutionary higher-order concept learning. In John R. Koza,editor, Late Breaking Papers at the Genetic Programming 1998 Conference, University of Wisconsin, Madison, Wisconsin, USA, 22–25 July 1998. Stanford University Bookstore.Google Scholar
  5. [5]
    R. King, S. Muggleton, S. Srinivasan, and M. Sternberg. Structure-activity relationships derived by machine learning: The use of atoms and their bond connectivities to predict mutagenicity in inductive logic programming. Proceedings of the National Academy of Sciences, 93: 438–442,1996.CrossRefGoogle Scholar
  6. [6]
    J.R. Koza. Concept formation and decision tree induction using the genetic programming paradigm. In H.-R Schwefel and R. Männer, editors, Parallel Problem Solving from Nature, pages 124–128, 1990.Google Scholar
  7. [7]
    J.R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge, Massachusetts, 1992.MATHGoogle Scholar
  8. [8]
    J.W. Lloyd. Declarative programming in escher. Technical Report CSTR-95-013, Department of Computer Science, University of Bristol, 1995.Google Scholar
  9. [9]
    T.M. Mitchell. Generalization as search. Artificial Intelligence, 18: 203–206, 1982.MathSciNetCrossRefGoogle Scholar
  10. [10]
    D.J. Montana. Strongly typed genetic programming. Evolutionary Computation, 3(2): 199–230, 1995.CrossRefGoogle Scholar
  11. [11]
    S. Muggleton and C.D. Page. Beyond first-order learning: Inductive learning with higher order logic. Technical Report PRG-TR-13-94, Oxford University Computing Laboratory, 1994.Google Scholar
  12. [12]
    S. Muggleton and L. De Raedt. Inductive logic programming: Theory and methods. Journal of Logic Programming, 19/20: 629–679, 1994.MathSciNetCrossRefGoogle Scholar
  13. [13]
    A. Srinivasan, S. Muggleton, R. King, and M. Sternberg. Mutagenesis: ILP experiments in a non-determinate biological domain. In S. Wrobel, editor, Proceedings of Fourth Inductive Logic Programming Workshop. Gesellschaft für Mathematik und Datenverarbeitung MBH, 1994. GMD-Studien Nr 237.Google Scholar
  14. [14]
    M. L. Wong and K. S. Leung. Genetic logic programming and applications. IEEE Expert, October 1995.Google Scholar

Copyright information

© Springer-Verlag Wien 1999

Authors and Affiliations

  • Claire J. Kennedy
    • 1
  • Christophe Giraud-Carrier
    • 1
  1. 1.Department of Computer ScienceUniversity of BristolBristolUK

Personalised recommendations