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European Conference on Genetic Programming

EuroGP 2008: Genetic Programming pp 170–181Cite as

The Performance of a Selection Architecture for Genetic Programming

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

Abstract

Hierarchical decomposition and reuse techniques are seen as making a vital contribution to the scalability of genetic programming systems. Existing techniques either try to identify and encapsulate useful code fragments as they evolve, or else they rely on intelligent prior deconstruction of the problem at hand. The alternative we propose is to base decomposition on a partitioning of the input test cases into subsets or ranges. To effect this, the program architecture of individuals is such that each subset is dealt with in an independently evolved branch, rooted at a selection node handling branch activation. Experimentation reveals that performance of systems employing this architecture is significantly better than that of more conventional systems.

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References

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

    MATH  Google Scholar 

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

    MATH  Google Scholar 

  3. Koza, J.R.: Simultaneous Discovery of Reusable Detectors and Subroutines Using Genetic Programming. In: Proc. 5th International Conf. Genetic Algorithms (ICGA-1993), pp. 295–302 (1993)

    Google Scholar 

  4. Angeline, P.J., Pollack, J.: Evolutionary Module Acquisition. In: Proc. 2nd Annual Conf. on Evolutionary Programming, La Jolla, CA, pp. 154–163 (1993)

    Google Scholar 

  5. Angeline, P.J., Pollack, J.: Coevolving High-Level Representations. In: Langton, C.G. (ed.) Artificial Life III, pp. 55–71. Addison-Wesley, Reading (1994)

    Google Scholar 

  6. Rosca, J.P., Ballard, D.H.: Discovery of Subroutines in Genetic Programming. In: Angeline, P., Kinnear Jr, K.E. (eds.) Advances in Genetic Programming 2, ch. 9, pp. 177–202. MIT Press, Cambridge (1996)

    Google Scholar 

  7. Roberts, S.C., Howard, D., Koza, J.R.: Evolving Modules in Genetic Programming by Subtree Encapsulation. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tetamanzi, A.G.B., Langdon, W.B. (eds.) EuroGP 2001. LNCS, vol. 2038, pp. 160–175. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  8. Miller, J.F., Thomson, P.: A Developmental Method for Growing Graphs and Circuits. In: Proc. 5th International Conf. on Evolvable Systems, Trondheim, Norway, pp. 93–104 (2003)

    Google Scholar 

  9. Walker, J.A., Miller, J.F.: Evolution and Acquisition of Modules in Cartesian Genetic Programming. In: Keijzer, M., O’Reilly, U.-M., Lucas, S.M., Costa, E., Soule, T. (eds.) EuroGP 2004. LNCS, vol. 3003, pp. 187–197. Springer, Heidelberg (2004)

    Google Scholar 

  10. Walker, J.A., Miller, J.F.: Improving the Performance of Module Acquisition in Cartesian Genetic Programming. In: Beyer, H.-G., O’Reilly, U.-M. (eds.) Proc. GECCO 2005, pp. 1649–1656. ACM Press, New York (2005)

    Chapter  Google Scholar 

  11. Gustafon, S.M.: Layered Learning in Genetic Programming for a Cooperative Robot Soccer Problem. M.S. Thesis, Dept. of Computing and Information Sciences, Kansas State University, USA (2000)

    Google Scholar 

  12. Gustafon, S.M., Hsu, W.H.: Layered Learning in Genetic Programming for a Cooperative Robot Soccer Problem. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tetamanzi, A.G.B., Langdon, W.B. (eds.) EuroGP 2001. LNCS, vol. 2038, pp. 291–301. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  13. Hsu, W.H., Gustafon, S.M.: Genetic Programming and Multi-Agent Layered Learning by Reinforcements. In: Proc. GECCO 2002, New York, NY, USA, pp. 764–771 (2002)

    Google Scholar 

  14. Hsu, W.H., Harmon, S.J., Rodriguez, E., Zhong, C.: Empirical Comparison of Incremental Reuse Strategies in Genetic Programming for Keep-Away Soccer. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, Springer, Heidelberg (2004)

    Google Scholar 

  15. Jackson, D., Gibbons, A.: Layered Learning for Boolean GP Problems. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds.) EuroGP 2007. LNCS, vol. 4445, pp. 148–159. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  16. Christensen, S., Oppacher, F.: An Analysis of Koza’s Computational Effort Statistic for Genetic Programming. In: Goos, G., et al. (eds.) EuroGP 2002. LNCS, vol. 2278, pp. 182–191. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Dept. of Computer Science, University of Liverpool, Liverpool, L69 3BX, United Kingdom

    David Jackson

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  1. David Jackson
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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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Jackson, D. (2008). The Performance of a Selection Architecture for Genetic Programming. 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_15

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  • DOI: https://doi.org/10.1007/978-3-540-78671-9_15

  • 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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