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Improving the Effectiveness of Genetic Programming Using Continuous Self-adaptation

  • Thomas D. Griffiths
  • Anikó Ekárt
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 732)

Abstract

Genetic Programming (GP) is a form of nature-inspired computing, introduced over 30 years ago, with notable success in problems such as symbolic regression. However, there remains a lot of relatively unexploited potential for solving hard, real-world problems. There is consensus in the GP community that the lack of effective real-world benchmark problems negatively impacts the quality of research [4]. When a GP system is initialised, a number of parameters must be provided. The optimal setup configuration is often not known, due to the fact that many of the values are problem and domain specific, meaning the GP system is unable to produce satisfactory results. We believe that the implementation of continuous self-adaptation, along with the introduction of tunable and suitably difficult benchmark problems, will allow for the creation of more robust GP systems that are resilient to failure.

Keywords

Genetic Programming Self-adaptation Benchmarks Tartarus 

References

  1. 1.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  2. 2.
    Taylor, T.: Requirements for open-ended evolution in natural and artificial systems. In: EvoEvo Workshop at the 13th European Conference on Artificial Life, ECAL 2015 (2015)Google Scholar
  3. 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. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    Teller, A.: The evolution of mental models. In: Advances in Genetic Programming, pp. 199–217 (1994)Google Scholar
  6. 6.
    Griffiths, T.D., Ekárt, A.: Improving the Tartarus problem as a benchmark in genetic programming. In: McDermott, J., Castelli, M., Sekanina, L., Haasdijk, E., García-Sánchez, P. (eds.) EuroGP 2017. LNCS, vol. 10196, pp. 278–293. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-55696-3_18CrossRefGoogle Scholar
  7. 7.
    Eiben, A.E., Hinterding, R., Michalewicz, Z.: Parameter control in evolutionary algorithms. IEEE Trans. Evol. Comput. 3(2), 124–141 (1999)CrossRefGoogle Scholar
  8. 8.
    Harding, S., Miller, J., Banzhaf, W.: Developments in Cartesian genetic programming: self-modifying CGP. Genet. Program Evolvable Mach. 11(3–4), 397–439 (2010)CrossRefGoogle Scholar
  9. 9.
    Kalkreuth, R., Rudolph, G., Krone, J.: Improving convergence in Cartesian genetic programming using adaptive crossover, mutation and selection. In: 2015 IEEE Symposium Series on Computational Intelligence, pp. 1415–1422 (2015)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Aston Lab for Intelligent Collectives Engineering (ALICE)Aston UniversityBirminghamUK

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