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Multi Objective Symbolic Regression

  • C. J. Hinde
  • N. Chakravorti
  • A. A. West
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 513)

Abstract

Symbolic regression has been a popular technique for some time. Systems typically evolve using a single objective fitness function, or where the fitness function is multi-objective the factors are combined using a weighted sum. This work uses a Non Dominated Sorting Strategy to rank the genomes. Using data derived from Swimming turns performed by elite athletes more information and better expressions can be generated than by using single, or even double objective functions. Symbolic regression, multi-objective, non dominated sorting, genetic programming.

Keywords

Pareto Front Probable Outlier Round Trip Time Fitness Evaluation Maximum Absolute Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Chow, J., Hay, J., Wilson, B., Imel, C.: Turning techniques of elite swimmers. J. Sports Sci. 2(3), 241–255 (1984)CrossRefGoogle Scholar
  2. 2.
    Nutonian: Eureqa desktop (2015). http://www.nutonian.com/products/eureqa/
  3. 3.
    Okuno, K.: Stroke characteristics of world class male swimmers in free style events of the \(9^{th}\) FINA world swimming championships 2001 Fukuoka. Biomech. Med. Swim. 157–162 (2003)Google Scholar
  4. 4.
    Puel, F., Morlier, J., Cid, M., Chollet, D., Hellard, P.: Biomechanical factors influencing tumble turn performance of elite female swimmers. Biomech. Med. Swim. 11, 155–157 (2010)Google Scholar
  5. 5.
    Tourny-Chollet, C., Chollet, D., Hogie, S., Papparodopoulos, C.: Kinematic analysis of butterfly turns of international and national swimmers. J. Sports Sci. 20(5), 383–390 (2001)CrossRefGoogle Scholar
  6. 6.
    Prins, A., Patz, J.H.: The influence of tuck index, depth of foot-plant, and wall contact time on the velocity of push-off in the freestyle flip turn. Methods 6(5), 46–46 (2006)Google Scholar
  7. 7.
    Araujo, L., Pereira, S., Gatti, R., Freitas, E., Jacomel, G., Roesler, H.A.: Analysis of the lateral push-off in the freestyle flip turn. J. Sports Sci. 28(11), 1175–1181 (2010)Google Scholar
  8. 8.
    Takahashi, G., Yoshida, A., Tsubakimoto, S., Miyashita, M.: Propulsive forces generated by swimmers during a turning motion. biomechanics and medicine in swimming. Biomech. Med. Swim. 192–198 (1983)Google Scholar
  9. 9.
    Blanksby, B., Skender, S., Elliott, B., McElroy, K., Landers, G.: An analysis of the rollover backstroke turn by agegroup swimmers. Sports Biomech. 1–14 (2004)Google Scholar
  10. 10.
    Blanksby, B.: Gaining on turns. In: Applied Proceedings of the XVIIth International Symposium on Biomechanics in Sports-Swimming, pp. 11–20 (1999)Google Scholar
  11. 11.
    Blanksby, B., Hodgkinson, J., Marshall, R.: Force-time characteristics of freestyle tumble turns by elite swimmers. S. Afr. J. Res. Sport Phys. Educ. Recreat. 19(1), 1–15 (1996)Google Scholar
  12. 12.
    Cossor, J., Blanksby, B., Elliott, B.: The influence of plyometric training on the freestyle tumble turn. J. Sci. Med. Sport 2(2), 106–116 (1999)CrossRefGoogle Scholar
  13. 13.
    Harrison, M.: Introduction to Formal Language Theory. Addison Wesley, London, UK (1978)zbMATHGoogle Scholar
  14. 14.
    Syswerda, G.: Uniform crossover in genetic algorithms. In: Schaffer, J. (ed.) Proceedings of Third International Conference on Genetic Algorithms, pp. 2–9. Morgan Kaufmann, Francisco, CA, USA (1989)Google Scholar
  15. 15.
    Jones, S., Hinde, C.: Uniform random crossover. In: Coghill, G.M. (ed.) Proceedings of the 2007 Workshop on Computational Intelligence. Aberdeen: University of Aberdeen (2007)Google Scholar
  16. 16.
    Hinde, C., Withall, M., Phillips, I., Jackson, T., Brown, S., Watson, R.: Train timetable generation using genetic algorithms. In: Filipe, J., Kacprzyk, J. (eds.) Proceedings of ICEC 2010, pp. 170–175. SciTePress, Valencia (2010)Google Scholar
  17. 17.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 181–197 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Loughborough UniversityLoughboroughUK

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