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A Metric for Genetic Programs and Fitness Sharing

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 1802)

Abstract

In the paper a metric for genetic programs is constructed. This metric reflects the structural difference of the genetic programs. It is used then for applying fitness sharing to genetic programs, in analogy with fitness sharing applied to genetic algorithms. The experimental results for several parameter settings are discussed. We observe that by applying fitness sharing the code growth of genetic programs could be limited.

Keywords

  • Genetic Algorithm
  • Genetic Program
  • Tournament Selection
  • Inductive Logic Programming
  • Label Tree

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.

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References

  1. Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming: An Introduction. Morgan Kaufmann, San Francisco (1998)

    MATH  Google Scholar 

  2. Bohnebeck, U., Horvath, T., Wrobel, S.: Term comparisons in first-order similarity measures. In: Page, D.L. (ed.) ILP 1998. LNCS (LNAI), vol. 1446, pp. 65–79. Springer, Heidelberg (1998)

    CrossRef  Google Scholar 

  3. Giles, J.R.: Introduction to the Analysis of Metric Spaces. Australian Mathematical Society Lecture Series (1987)

    Google Scholar 

  4. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  5. Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  6. Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched Pareto genetic algorithm for multiobjective optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation, pp. 82–87 (1994)

    Google Scholar 

  7. Iba, H., de Garis, H., Sato, T.: Genetic programming using a minimum description length principle. In: Kinnear, K.E. (ed.) Advances in Genetic Programming, pp. 265–284. MIT Press, Cambridge (1994)

    Google Scholar 

  8. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  9. Lu, S.-Y.: The tree-to-tree distance and its application to cluster analysis. IEEE Transactions on PAMI 1(2), 219–224 (1979)

    MATH  Google Scholar 

  10. Mahfoud, S.: Niching methods for genetic algorithms, Illigal report 95001, University of Illinois at Urbana-Champaign (1995)

    Google Scholar 

  11. Nienhuys-Cheng, S.-H.: Distance between Herbrand interpretations: a measure for approximations to a target concept. In: Džeroski, S., Lavrač, N. (eds.) ILP 1997. LNCS (LNAI), vol. 1297, pp. 213–226. Springer, Heidelberg (1997)

    Google Scholar 

  12. Oei, C.K., Goldberg, D.E., Chang, S.J.: Tournament selection, niching and the preservation of diversity, Illigal report 91011, University of Illinois at Urbana-Champaign (1991)

    Google Scholar 

  13. Selkow, S.M.: The tree-to-tree editing problem. Information Processing Letters 6(6), 184–186 (1977)

    MATH  CrossRef  MathSciNet  Google Scholar 

  14. Soule, T., Foster, J.A., Dickinson, J.: Code growth in genetic programming. In: Koza, J.R., Goldberg, D.E., Fogel, D.B., Riolo, R.L. (eds.) Genetic Programming 1996: Proceedings of the First Annual Conference, pp. 215–223 (1996)

    Google Scholar 

  15. Tai, K.-C.: The tree-to-tree correction problem. Journal of the ACM 26(3), 422–433 (1979)

    MATH  CrossRef  MathSciNet  Google Scholar 

  16. Yin, X., Germay, N.: A fast genetic algorithm with sharing scheme using cluster analysis methods in multimodal function optimization. In: Albrecht, R.F., Steele, N.C., Reeves, C.R. (eds.) Artificial Neural Nets and Genetic Algorithms, pp. 450–457 (1993)

    Google Scholar 

  17. Zhang, B.-T., Muhlenbein, H.: Balancing accuracy and parsimony in genetic programming. Evolutionary Computation 3(1), 17–38 (1995)

    CrossRef  Google Scholar 

  18. Zhang, K., Statman, R., Shasha, D.: On the editing distance between unordered labeled trees. Information Processing Letters 42, 133–139 (1992)

    MATH  CrossRef  MathSciNet  Google Scholar 

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Ekárt, A., Németh, S.Z. (2000). A Metric for Genetic Programs and Fitness Sharing. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds) Genetic Programming. EuroGP 2000. Lecture Notes in Computer Science, vol 1802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-46239-2_19

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  • DOI: https://doi.org/10.1007/978-3-540-46239-2_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67339-2

  • Online ISBN: 978-3-540-46239-2

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