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Improving the Tartarus Problem as a Benchmark in Genetic Programming

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Genetic Programming (EuroGP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10196))

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Abstract

For empirical research on computer algorithms, it is essential to have a set of benchmark problems on which the relative performance of different methods and their applicability can be assessed. In the majority of computational research fields there are established sets of benchmark problems; however, the field of genetic programming lacks a similarly rigorously defined set of benchmarks. There is a strong interest within the genetic programming community to develop a suite of benchmarks. Following recent surveys [7], the desirable characteristics of a benchmark problem are now better defined. In this paper the Tartarus problem is proposed as a tunably difficult benchmark problem for use in Genetic Programming. The justification for this proposal is presented, together with guidance on its usage as a benchmark.

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Notes

  1. 1.

    http://eu-robotics.net/robotics_league.

  2. 2.

    http://www.eurathlon.eu. An outdoor robotics challenge for land, sea and air.

References

  1. Korkmaz, E.E., Üçoluk, G.: Design and usage of a new benchmark problem for genetic programming. In: Yazıcı, A., Şener, C. (eds.) ISCIS 2003. LNCS, vol. 2869, pp. 561–567. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  2. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection (1992)

    Google Scholar 

  3. McDermott, J., White, D.R., Luke, S., Manzoni, L., Castelli, M., Vanneschi, L., Jaskowski, W., Krawiec, K., Harper, R., De Jong, K., O’Reilly, U.M.: Genetic programming needs better benchmarks. In: Soule, T., et al. (eds.) Proceedings of the 14th International Conference on Genetic and Evolutionary Computation, GECCO 2012, pp. 791–798 (2012)

    Google Scholar 

  4. Sendhoff, B., Roberts, M., Yao, X.: Evolutionary computation benchmarking repository. IEEE Comput. Intell. Mag. 1, 50–60 (2006)

    Google Scholar 

  5. Teller, A.: The evolution of mental models. In: Kinnear Jr. K.E. (ed.) Advances in Genetic Programming, pp. 199–217 (1994)

    Google Scholar 

  6. Vanneschi, L., Castelli, M., Manzoni, L.: The K landscapes: a tunably difficult benchmark for genetic programming. In: Krasnogor, N., et al. (eds.) Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, pp. 1467–1474 (2011)

    Google Scholar 

  7. White, D.R., McDermott, J., Castelli, M., Manzoni, L., Goldman, B.W., Kronberger, G., Jaśkowski, W., O’Reilly, U.M., Luke, S.: Better GP benchmarks: community survey results and proposals. Genet. Program Evolvable Mach. 14(1), 3–29 (2013)

    Article  Google Scholar 

  8. Woodward, J., Martin, S., Swan, J.: Benchmarks that matter for genetic programming. In: Woodward, J., et al. (eds.) 4th Workshop on Evolutionary Computation for the Automated Design of Algorithms, GECCO 2014, pp. 1397–1404 (2014)

    Google Scholar 

  9. Jiju, A.: Design of Experiments for Engineers and Scientists. Elsevier, Amsterdam (2003)

    Google Scholar 

  10. Koza, J.R.: Scalable learning in genetic programming using automatic function definition. In: Kinnear Jr. K.E. (ed.) Advances in Genetic Programming, pp. 99–117 (1994)

    Google Scholar 

  11. Ashlock, D., Joenks, M.: ISAc lists: a different program induction method. In: Koza, J.R., et al. (eds.) Proceedings of the Second Annual Conference on Genetic Programming, pp. 18–26 (1998)

    Google Scholar 

  12. Ashlock, D., Freeman, J.: A pure finite state baseline for Tartarus. In: Proceedings of the 2000 Congress on Evolutionary Computation, pp. 1223–1230 (2000)

    Google Scholar 

  13. Dick, G.: An effective parse tree representation for Tartarus. In: Blum, C., et al. (eds.) Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013, pp. 1397–1404 (2013)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Aston Lab for Intelligent Collectives Engineering (ALICE), Aston University, Aston Triangle, Birmingham, B4 7ET, UK

    Thomas D. Griffiths & Anikó Ekárt

Authors
  1. Thomas D. Griffiths
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  2. Anikó Ekárt
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Corresponding author

Correspondence to Thomas D. Griffiths .

Editor information

Editors and Affiliations

  1. University College Dublin , Dublin, Ireland

    James McDermott

  2. Universidade Nova de Lisboa , Lisbon, Portugal

    Mauro Castelli

  3. Brno University of Technology , Brno, Czech Republic

    Lukas Sekanina

  4. Vrije Universiteit Amsterdam , Amsterdam, The Netherlands

    Evert Haasdijk

  5. University of Cádiz , Cádiz, Spain

    Pablo García-Sánchez

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Griffiths, T.D., Ekárt, A. (2017). Improving the Tartarus Problem as a Benchmark in Genetic Programming. In: McDermott, J., Castelli, M., Sekanina, L., Haasdijk, E., García-Sánchez, P. (eds) Genetic Programming. EuroGP 2017. Lecture Notes in Computer Science(), vol 10196. Springer, Cham. https://doi.org/10.1007/978-3-319-55696-3_18

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  • DOI: https://doi.org/10.1007/978-3-319-55696-3_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55695-6

  • Online ISBN: 978-3-319-55696-3

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