Cartesian Genetic Programming Based Optimization and Prediction

  • Kisung Seo
  • Byeongyong Hyeon
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 275)

Abstract

This paper introduces a CGP (Cartesian Genetic Programming) based optimization and prediction techniques. In order to provide a superior search for optimization and a robust model for prediction, a nonlinear and symbolic regression method using CGP is suggested. CGP uses as genotype a linear string of integers that are mapped to a directed graph. Therefore, some evolved modules for regression polynomials in CGP network can be shared and reused among multiple outputs for prediction of neighborhood precipitation. To investigate the effectiveness of the proposed approach, experiments on gait generation for quadruped robots and prediction of heavy precipitation for local area of Korean Peninsular were executed.

Keywords

Cartesian Genetic Programming gait optimization heavy rain prediction symbolic regression 

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References

  1. 1.
    Miller, J.F., Thomson, P.: Cartesian Genetic Programming. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 121–132. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  2. 2.
    Miller, J.F., Jo, D., Vassilev, V.K.: Principles in the Evolutionary Design of Digital Circuits - Part I. Genetic Programming and Evolvable Machines 1, 8–35 (2000)Google Scholar
  3. 3.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Natural Selection. MIT Press, Cambridge (1992)MATHGoogle Scholar
  4. 4.
    Hornby, G.S., Takamura, S., Yamamoto, T., Fujita, M.: Autonomous evolution of dynamic gaits with two quadruped robots. IEEE Trans. Robotics 21, 402–410 (2005)CrossRefGoogle Scholar
  5. 5.
    Seo, K., Hyun, S., Goodman, E.: Genetic Programming-Based Automatic Gait Generation in Joint Space for a Quadruped Robot. Advanced Robotics 24, 2199–2214 (2010)CrossRefGoogle Scholar
  6. 6.
    Seo, K., Hyun, S.: A Comparative Study between Genetic Algorithm and Genetic Programming Based Gait Generation Methods for Quadruped Robots. In: Di Chio, C., et al. (eds.) EvoApplicatons 2010, Part I. LNCS, vol. 6024, pp. 352–360. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Dong, H., Zhao, M., Zhang, J., Shi, Z., Zhang, N.: Gait planning of quadruped robot based on third-order spline interpolation. In: Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2006), pp. 5756–5761. IEEE Press, China (2006)CrossRefGoogle Scholar
  8. 8.
    Bosilovich, M.G., Chen, J., Robertson, F.R., Adler, R.F.: Evaluation of Global Precipitation in Reanalyses. Journal of Applied Meteorology and Climatology 47(9), 2279–2299 (2008)CrossRefGoogle Scholar
  9. 9.
    Hamill, T.M., Whitaker, J.S.: Probabilistic Quantitative Precipitation Forecasts Based on Reforecast Analogs: Theory and Application. Monthly Weather Review 134(11), 3209–3229 (2006)CrossRefGoogle Scholar
  10. 10.
    Kim, Y., Ham, S.: Heavy Rainfall prediction using convective instability index. Journal of the Korean Society for Aeronautical Science and Flight Operation 17(1), 17–23 (2009)MathSciNetGoogle Scholar
  11. 11.
    Hohl, L., Tellez, R., Michel, O., Ijspeert, A.J.: Aibo and Webots: Simulation, wireless remote control and controller transfer. Robotics and Autonomous Systems 54, 472–485 (2006)CrossRefGoogle Scholar
  12. 12.
    Korean Meteorological Society, Introduction to Atmospheric Science. Sigma Press (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kisung Seo
    • 1
  • Byeongyong Hyeon
    • 1
  1. 1.Dept. of Electronic EngineeringSeokyeong UniversitySeoulKorea

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