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A Novel Approach to Machine Discovery: Genetic Programming and Stochastic Grammars

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Inductive Logic Programming (ILP 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2583))

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Abstract

The application of Genetic Programming (GP) to the discovery of empirical laws most often suffers from two limitations. The first one is the size of the search space; the second one is the growth of non-coding segments, the introns, which exhausts the memory resources as GP evolution proceeds.

These limitations are addressed by combining Genetic Programming and Stochastic Grammars. On one hand, grammars are used to represent prior knowledge; for instance, context-free grammars can be used to enforce the discovery of dimensionally consistent laws, thereby significantly restricting GP search space. On the other hand, in the spirit of distribution estimation algorithms, the grammar is enriched with derivation probabilities. By exploiting such probabilities, GP avoids the intron phenomenon.

The approach is illustrated on a real-world like problem, the identification of behavioral laws in Mechanics.

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References

  1. S. Baluja and R. Caruana. Removing the genetics from the standard genetic algorithms. In A. Prieditis and S. Russel, eds, Proc. of the 12th Int Conf. on Machine Learning, pages 38–46. Morgan Kaufmann, 1995.

    Google Scholar 

  2. W. Banzhaf, P. Nordin, R.E. Keller, and F.D. Francone. Genetic Programming-An Introduction On the Automatic Evolution of Computer Programs and Its Applications. Morgan Kaufmann, 1998.

    Google Scholar 

  3. T. Bäck. Evolutionary Algorithms in theory and practice. New-York Oxford University Press, 1995.

    Google Scholar 

  4. J.M. Daida. Challenges with verification, repeatability, and meaningful comparison in genetic programming: Gibson’s magic. In Proc. of the Genetic and Evolutionary Conf. 99, pages 1069–1076. Morgan Kaufmann, 1999.

    Google Scholar 

  5. J. Duffy and J. Engle-Warnick. Using symbolic regression to infer strategies from experimental data. In Evolutionary Computation in Economics and Finance. Springer Verlag, 1999.

    Google Scholar 

  6. F. Gruau. On using syntactic constraints with genetic programming. In P.J. Angeline and K.E. Kinnear Jr., eds, Advances in Genetic Programming II, pages 377–394. MIT Press, 1996.

    Google Scholar 

  7. C. Z. Janikow. A knowledge-intensive genetic algorithm for supervised learning. Machine Learning, 13:189–228, 1993.

    Article  Google Scholar 

  8. J. R. Koza. Genetic Programming: On the Programming of Computers by means of Natural Evolution. MIT Press Massachusetts, 1992.

    Google Scholar 

  9. W. B. Langdon and R. Poli. Fitness causes bloat. In Soft Computing in Engineering Design and Manufacturing, pages 13–22. Springer Verlag, 1997.

    Google Scholar 

  10. P. Langley, H.A. Simon, and G.L. Bradshaw. Rediscovering chemistry with the BACON system. In R.S Michalski, J.G. Carbonell, and T.M. Mitchell, eds, Machine Learning: an artificial intelligence approach, volume 1. Morgan Kaufmann, 1983.

    Google Scholar 

  11. P. Larranaga and J. A. Lozano. Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation. Kluwer Academic Publishers, 2001.

    Google Scholar 

  12. B. McKay, M.J. Willis, and G.W. Barton. Using a tree structures genetic algorithm to perform symbolic regression. In IEEE Conf. publications, n. 414, pages 487–492, 1995.

    Google Scholar 

  13. David J. Montana. Strongly typed genetic programming. Evolutionary Computation, 3(2):199–230, 1995.

    Article  Google Scholar 

  14. P. Nordin, W. Banzhaf, and F.D. Francone. Introns in nature and in simulated structure evolution. In D. Lundh, B. Olsson, and A. Narayanan, eds, Biocomputing and Emergent Computation, pages 22–35. World Scientific, 1997.

    Google Scholar 

  15. M. O’Neill and C. Ryan, eds. Grammatical Evolution. Genetic and Evolutionary Conf. Workshop, GECCO 2002, 2002.

    Google Scholar 

  16. M. Pelikan, D.E. Goldberg and E. Cantu-Paz. BOA: the bayesian optimization algorithm. In Genetic and Evolutionary Conf., GECCO 1999, pages 525–532. Morgan Kaufmann, 1999.

    Google Scholar 

  17. N. J. Radcliffe. Equivalence class analysis of genetic algorithms. Complex Systems, 5:183–20, 1991.

    MATH  MathSciNet  Google Scholar 

  18. A. Ratle and M. Sebag. Genetic programming and domain knowledge: Beyond the limitations of grammar-guided machine discovery. In M. Schoenauer et al., editor, Proc. of the 6th Conf. on Parallel Problems Solving from Nature, pages 211–220. Springer-Verlag, LNCS 1917, 2000.

    Google Scholar 

  19. C. Ryan, J.J. Collins, and M. O’Neill. Grammatical evolution: Evolving programs for an arbitrary language. In W. Banzhaf, R. Poli, M. Schoenauer, and T.C. Fogarty, eds, Genetic Programming, First Eur. Workshop, EuroGP98, pages 83–96. Springer Verlag LNCS 1391, 1998.

    Google Scholar 

  20. R. Salustowicz and J. Schmidhuber. Evolving structured programs with hierarchical instructions and skip nodes. In J. Shavlik, editor, Proc. of the 15th Int Conf. on Machine Learning, pages 488–496. Morgan Kaufmann, 1998.

    Google Scholar 

  21. M. Sebag and A. Ducoulombier. Extending population-based incremental learning to continuous search spaces. In Th. Bäck, G. Eiben, M. Schoenauer, and H.-P. Schwefel, eds, Proc. of the 5th Conf. on Parallel Problems Solving from Nature, pages 418–427. Springer Verlag, 1998.

    Google Scholar 

  22. L. Todorovski and S. Dzeroski. Using domain knowledge on population dynamics modeling for equation discovery. In P. Flach and L. De Raedt, eds, Proc. of the 12th Eur. Conf. on Machine Learning, pages 478–490. Springer Verlag LNAI 2167, 2001.

    Google Scholar 

  23. R. Valdes-Perez. Machine discovery in chemistry: New results. Artificial Intelligence, 4:191–201, 1995.

    Article  Google Scholar 

  24. I.M. Ward. Mechanical Properties of Solid Polymers. Wiley Chichester, 1985.

    Google Scholar 

  25. T. Washio, H. Motoda, and Y. Niwa. Discovering admissible simultaneous equation models from observed data. In L. de Raedt and P. Flach, eds, Proc. of the 12th Eur. Conf. on Machine Learning, pages 539–551. Springer Verlag LNAI 2167, 2001.

    Google Scholar 

  26. P.A. Whigham. Inductive bias and genetic programming. In IEEE Conf. publications, n. 414, pages 461–466, 1995.

    Google Scholar 

  27. Byoung-Tak Zhang and Heinz Mühlenbein. Balancing accuracy and parsimony in genetic programming. Evolutionary Computation, 3(1):17–38, 1995.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. LRMA, Institut Supérieur de l’Automobile et des Transports, 58027, Nevers, France

    Alain Ratle

  2. LRI CNRS UMR 8623, Université Paris Sud, 91405, Orsay, France

    Michèle Sebag

Authors
  1. Alain Ratle
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  2. Michèle Sebag
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Editor information

Editors and Affiliations

  1. School of Information Technology and Engineering, University of Ottawa, 800 King Edward Ave., K1N 6N5, Ottawa, ON, Canada

    Stan Matwin

  2. School of Computer Science and Engineering, University of New South Wales, 2052, Sydney, NSW, Australia

    Claude Sammut

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© 2003 Springer-Verlag Berlin Heidelberg

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Ratle, A., Sebag, M. (2003). A Novel Approach to Machine Discovery: Genetic Programming and Stochastic Grammars. In: Matwin, S., Sammut, C. (eds) Inductive Logic Programming. ILP 2002. Lecture Notes in Computer Science(), vol 2583. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36468-4_14

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  • DOI: https://doi.org/10.1007/3-540-36468-4_14

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00567-4

  • Online ISBN: 978-3-540-36468-9

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