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European Conference on Genetic Programming

EuroGP 2009: Genetic Programming pp 256–267Cite as

Behavioural Diversity and Filtering in GP Navigation Problems

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5481))

Abstract

Promoting and maintaining diversity in a population is considered an important element of evolutionary computing systems, and genetic programpming (GP) is no exception. Diversity metrics in GP are usually based on structural program characteristics, but even when based on behaviour they almost always relate to fitness. We deviate from this in two ways: firstly, by considering an alternative view of diversity based on the actual activity performed during execution, irrespective of fitness; and secondly, by examining the effects of applying associated diversity-enhancing algorithms to the initial population only. Used together with an extension to this approach that provides for additional filtering of candidate population members, the techniques offer significant performance improvements when applied to the Santa Fe artificial ant problem and a maze navigation problem.

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References

  1. McPhee, N.F., Hopper, N.J.: Analysis of Genetic Diversity through Program History. In: Banzhaf, W., et al. (eds.) Proc. Genetic and Evolutionary Computation Conf., Florida, USA, pp. 1112–1120 (1999)

    Google Scholar 

  2. Daida, J.M., Ward, D.J., Hilss, A.M., Long, S.L., Hodges, M.R., Kriesel, J.T.: Visualizing the Loss of Diversity in Genetic Programming. In: Proc. IEEE Congress on Evolutionary Computation, Portland, Oregon, USA, pp. 1225–1232 (2004)

    Google Scholar 

  3. Hien, N.T., Hoai, N.X.: A Brief Overview of Population Diversity Measures in Genetic Programming. In: Pham, T.L., et al. (eds.) Proc. 3rd Asian-Pacific Workshop on Genetic Programming, Hanoi, Vietnam, pp. 128–139 (2006)

    Google Scholar 

  4. Burke, E., Gustafson, S., Kendall, G., Krasnogor, N.: Advanced Population Diversity Measures in Genetic Programming. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 341–350. Springer, Heidelberg (2002)

    Google Scholar 

  5. Burke, E., Gustafson, S., Kendall, G.: Diversity in Genetic Programming: An Analysis of Measures and Correlation with Fitness. IEEE Transactions on Evolutionary Computation 8(1), 47–62 (2004)

    Article  Google Scholar 

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

    MATH  Google Scholar 

  7. de Jong, E.D., Watson, R.A., Pollack, J.B.: Reducing Bloat and Promoting Diversity using Multi-Objective Methods. In: Spector, L., et al. (eds.) Proc. Genetic Evolutionary Computation Conf., San Francisco, CA, USA, pp. 11–18 (2001)

    Google Scholar 

  8. Wyns, B., de Bruyne, P., Boullart, L.: Characterizing Diversity in Genetic Programming. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds.) EuroGP 2006. LNCS, vol. 3905, pp. 250–259. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  9. Rosca, J.P.: Genetic Programming Exploratory Power and the Discovery of Functions. In: McDonnell, J.R., et al. (eds.) Proc. 4th Conf. Evolutionary Programming, San Diego, CA, USA, pp. 719–736 (1995)

    Google Scholar 

  10. Rosca, J.P.: Entropy-Driven Adaptive Representation. In: Rosca, J.P. (ed.) Proc. Workshop on Genetic Programming: From Theory to Real-World Applications, Tahoe City, CA, USA, pp. 23–32 (1995)

    Google Scholar 

  11. D’haeseleer, P., Bluming, J.: Effects of Locality in Individual and Population Evolution. In: Kinnear, K.E., et al. (eds.) Advances in Genetic Programming, ch. 8, pp. 177–198. MIT Press, Cambridge (1994)

    Google Scholar 

  12. Ryan, C.: Pygmies and Civil Servants. In: Kinnear, K.E., et al. (eds.) Advances in Genetic Programming, ch. 11, pp. 243–263. MIT Press, Cambridge (1994)

    Google Scholar 

  13. Looks, M.: On the Behavioural Diversity of Random Programs. In: Thierens, D., et al. (eds.) Proc. Genetic and Evolutionary Computing Conf. (GECCO 2007), London, England, UK, pp. 1636–1642 (2007)

    Google Scholar 

  14. Daida, J.M.: Towards Identifying Populations that Increase the Likelihood of Success in Genetic Programming. In: Beyer, H.-G., et al. (eds.) Proc. Genetic and Evolutionary Computing Conf. (GECCO 2005), Washington DC, USA, pp. 1627–1634 (2005)

    Google Scholar 

  15. Langdon, W.B., Poli, R.: Why Ants are Hard. In: Koza, J.R., et al. (eds.) Genetic Programming 1998: Proceedings of the Third Annual Conference, pp. 193–201. Morgan Kaufman, San Francisco (1998)

    Google Scholar 

  16. Soule, T.: Code Growth in Genetic Programming. Ph.D Thesis, University of Idaho (1998)

    Google Scholar 

  17. Langdon, W.B., Soule, T., Poli, R., Foster, J.A.: The Evolution of Size and Shape. In: Spector, L., et al. (eds.) Advances in Genetic Programming, vol. 3, pp. 163–190. MIT Press, Cambridge (1999)

    Google Scholar 

  18. Jackson, D.: Dormant Program Nodes and the Efficiency of Genetic Programming. In: Beyer, H.-G., et al. (eds.) Proc. Genetic and Evolutionary Computing Conf. (GECCO 2005), Washington DC, USA, pp. 1745–1751 (2005)

    Google Scholar 

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Author information

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

Editors and Affiliations

  1. Department of Informatics, Systems and Communication (D.I.S.Co.), University of Milano-Bicocca, viale Sarca 336-U14, 20126, Milano, Italy

    Leonardo Vanneschi

  2. GE Global Research, NY 12309, Niskayuna, USA

    Steven Gustafson

  3. Department of Computer Engineering, University of Coimbra, Polo II - Pinhal de Marrocos, 3030, Coimbra, Portugal

    Alberto Moraglio

  4. Institute of High Performance Computing and Networking, National Research Council of Italy (ICAR - CNR), Via P. Castellino 111, 80131, Napoli, Italy

    Ivanoe De Falco

  5. Wilhelm Schickard Institut für Informatik, Eberhard Karls Universität Tübingen, Abt. Rechnerarchitektur, Sand 1, 72076, Tübingen, Germany

    Marc Ebner

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© 2009 Springer-Verlag Berlin Heidelberg

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Jackson, D. (2009). Behavioural Diversity and Filtering in GP Navigation Problems. In: Vanneschi, L., Gustafson, S., Moraglio, A., De Falco, I., Ebner, M. (eds) Genetic Programming. EuroGP 2009. Lecture Notes in Computer Science, vol 5481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01181-8_22

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  • DOI: https://doi.org/10.1007/978-3-642-01181-8_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01180-1

  • Online ISBN: 978-3-642-01181-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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