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European Conference on Genetic Programming

EuroGP 2008: Genetic Programming pp 206–217Cite as

A Linear Estimation-of-Distribution GP System

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4971))

Abstract

We present N-gram GP, an estimation of distribution algorithm for the evolution of linear computer programs. The algorithm learns and samples a joint probability distribution of triplets of instructions (or 3-grams) at the same time as it is learning and sampling a program length distribution. We have tested N-gram GP on symbolic regressions problems where the target function is a polynomial of up to degree 12 and lawn-mower problems with lawn sizes of up to 12×12. Results show that the algorithm is effective and scales better on these problems than either linear GP or simple stochastic hill-climbing.

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References

  1. Abbass, H., Hoai, N., McKay, R.: AntTAG: A new method to compose computer programs using colonies of ants. In: IEEE Congress on Evolutionary Computation (2002)

    Google Scholar 

  2. Baluja, S., Caruana, R.: Removing the genetics from the standard genetic algorithm. In: Prieditis, A., Russell, S. (eds.) Machine Learning: Proceedings of the Twelfth International Conference, pp. 38–46. Morgan Kaufmann Publishers, San Francisco (1995)

    Google Scholar 

  3. Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge (1994)

    MATH  Google Scholar 

  4. Larrañaga, P., Lozano, J.A.: Estimation of Distribution Algorithms, A New Tool for Evolutionary Computation. Kluwer Academic Publishers, Dordrecht (2002)

    MATH  Google Scholar 

  5. Manning, C., Schütze, H.: Foundations of statistical natural language processing. MIT Press, Cambridge (1999)

    MATH  Google Scholar 

  6. Mühlenbein, H., Mahnig, T.: Convergence theory and application of the factorized distribution algorithm. Journal of Computing and Information Technology 7(1), 19–32 (1999)

    Google Scholar 

  7. Nordin, P.: A compiling genetic programming system that directly manipulates the machine code. In: Kinnear Jr., K.E. (ed.) K, ch. 14, pp. 311–331. MIT Press, Cambridge (1994)

    Google Scholar 

  8. Poli, R., McPhee, N.F.: A linear estimation-of-distribution GP system. Tech. Report CES-479, Dept. of Computing and Electronic Systems, University of Essex (January 2008)

    Google Scholar 

  9. Rabiner, L.: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)

    Article  Google Scholar 

  10. Ratle, A., Sebag, M.: Avoiding the bloat with probabilistic grammar-guided genetic programming. In: Collet, P., Fonlupt, C., Hao, J.-K., Lutton, E., Schoenauer, M. (eds.) EA 2001. LNCS, vol. 2310, pp. 255–266. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  11. Salustowicz, R.P., Schmidhuber, J.: Probabilistic incremental program evolution. Evolutionary Computation 5(2), 123–141 (1997)

    Article  Google Scholar 

  12. Sastry, K., Goldberg, D.E.: Probabilistic model building and competent genetic programming. In: Riolo, R.L., Worzel, B. (eds.) Genetic Programming Theory and Practise, ch. 13, pp. 205–220. Kluwer, Dordrecht (2003)

    Google Scholar 

  13. Shan, Y., McKay, R.I., Abbass, H.A., Essam, D.: Program evolution with explicit learning: a new framework for program automatic synthesis. In: Sarker, R., Reynolds, R., Abbass, H., Tan, K.C., McKay, B., Essam, D., Gedeon, T. (eds.) Proceedings of the 2003 Congress on Evolutionary Computation CEC 2003, Canberra, December 2003, pp. 1639–1646. IEEE Press, Los Alamitos (2003)

    Chapter  Google Scholar 

  14. Shan, Y., McKay, R.I., Essam, D., Abbass, H.A.: A survey of probabilistic model building genetic programming. In: Pelikan, M., Sastry, K., Cantu-Paz, E. (eds.) Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications, Springer, Heidelberg (2006)

    Google Scholar 

  15. Suen, C.Y.: n-gram statistics for natural language understanding and text processing. IEEE Transactions on Pattern Analysis and Machine Intelligence 1(2), 164–172 (1979)

    Article  Google Scholar 

  16. Yanai, K., Iba, H.: Estimation of distribution programming based on bayesian network. In: Sarker, R., Reynolds, R., Abbass, H., Tan, K.C., McKay, B., Essam, D., Gedeon, T. (eds.) Proceedings of the 2003 Congress on Evolutionary Computation CEC 2003, pp. 1618–1625. IEEE Press, Los Alamitos (2003)

    Chapter  Google Scholar 

  17. Yanai, K., Iba, H.: Program evolution by integrating EDP and GP. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 774–785. Springer, Heidelberg (2004)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computing and Electronic Systems, University of Essex, UK

    Riccardo Poli

  2. Division of Science and Mathematics, University of Minnesota, Morris, USA

    Nicholas Freitag McPhee

Authors
  1. Riccardo Poli
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  2. Nicholas Freitag McPhee
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Editor information

Michael O’Neill Leonardo Vanneschi Steven Gustafson Anna Isabel Esparcia Alcázar Ivanoe De Falco Antonio Della Cioppa Ernesto Tarantino

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© 2008 Springer-Verlag Berlin Heidelberg

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Poli, R., McPhee, N.F. (2008). A Linear Estimation-of-Distribution GP System. In: O’Neill, M., et al. Genetic Programming. EuroGP 2008. Lecture Notes in Computer Science, vol 4971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78671-9_18

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  • DOI: https://doi.org/10.1007/978-3-540-78671-9_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78670-2

  • Online ISBN: 978-3-540-78671-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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