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Improvement of Genetic Programming Based Nonlinear Regression Using ADF and Application for Prediction MOS of Wind Speed

ADF를 사용한 유전프로그래밍 기반 비선형 회귀분석 기법 개선 및 풍속 예보 보정 응용

  • Oh, Seungchul (Dept. of Electronics Engineering, Seokyeong University) ;
  • Seo, Kisung (Dept. of Electronics Engineering, Seokyeong University)
  • Received : 2015.09.29
  • Accepted : 2015.11.07
  • Published : 2015.12.01

Abstract

A linear regression is widely used for prediction problem, but it is hard to manage an irregular nature of nonlinear system. Although nonlinear regression methods have been adopted, most of them are only fit to low and limited structure problem with small number of independent variables. However, real-world problem, such as weather prediction required complex nonlinear regression with large number of variables. GP(Genetic Programming) based evolutionary nonlinear regression method is an efficient approach to attach the challenging problem. This paper introduces the improvement of an GP based nonlinear regression method using ADF(Automatically Defined Function). It is believed ADFs allow the evolution of modular solutions and, consequently, improve the performance of the GP technique. The suggested ADF based GP nonlinear regression methods are compared with UM, MLR, and previous GP method for 3 days prediction of wind speed using MOS(Model Output Statistics) for partial South Korean regions. The UM and KLAPS data of 2007-2009, 2011-2013 years are used for experimentation.

Keywords

References

  1. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA, 1989.
  2. J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, The MIT Press, 1992.
  3. J. R. Koza, 1994 : Genetic Programming II: Automatic Discovery of Reusable Programs, The MIT Press, 1994.
  4. J. R. Koza, F. H. Bennett, D. Andre and M. A. Keane, III, Darwinian Invention and Problem Solving, Morgan Kaufmann Publishers, USA, 1999.
  5. K. Seo S. Hyun E. D. Goodman, "Genetic Programming-Based Automatic Gait Generation in Joint Space for a Quadruped Robot," Advanced Robotics, Vol. 24, No. 15, pp. 2199-2214. 2010. https://doi.org/10.1163/016918610X534312
  6. S. Luke, "Issues in Scaling Genetic Programming: Breeding Strategies, Tree Generation, and Code Bloat," PhD of University of Maryland, 2000.
  7. R. Poli and J. Page, "Solving High Order Boolean Parity Problems with Smooth Uniform Crossover, Sub Machine Code GP and Demes," Genetic Programming and Evoluvable Machines, Volume 1, Issue 1/2, pp.37-56, April 2000. https://doi.org/10.1023/A:1010068314282
  8. Nguyen, X. Hoai, B. McKay and D. Essam, "Representation and structural Difficulty in Genetic Programming," Evolutionary computation, IEEE Transactions on Volume 10, Issue 2, pp.157-166, April 2006. https://doi.org/10.1109/TEVC.2006.871252
  9. K. Seo, C. Pang, "Tree-Structure-Aware Genetic Operators in Genetic Programming", JEET(Journal of Electrical Engineering and Technology), vol.9, no.2, pp.755-761, March 2014. https://doi.org/10.5370/JEET.2014.9.2.755
  10. R. Poli, W. B. Langdon, N. F. McPhee, A Field Guide to Genetic Programming, Lulu Enterprises,, 2008.
  11. W. Gang and T. Soule, "How to Choose Appropriate Function Sets for GP," Genetic Programming 7th European Conference, EuroGP 2004, Proceedings, LNCS, Vol. 3003, pp. 198-207, Springer-Verlag, 2004.
  12. Korea Meteorological Administration, http://www.kma.go.kr.
  13. H. R. Glahn, D. A. Lowry, "The use of model output statistics (MOS) in objective weather forecasting", J. Appl. Meteor., 11, pp. 1203-1211, 1972. https://doi.org/10.1175/1520-0450(1972)011<1203:TUOMOS>2.0.CO;2
  14. United Kingdom Met Office's website : http://www.metoffice.gov.uk
  15. K. Seo, B. Hyeon, S. Hyun, and Y. Lee, "Genetic Programming-Based Model Output Statistics for Short-Range Temperature Prediction", Lecture Notes in Computer Science, Springer-Verlag, Vol. 7835, pp. 122-131, 2013
  16. B. Hyeon, K. Seo, Y. Lee, "Evolutionary Nonlinear Regression Based Compensation Technique for Short-range Prediction of Wind Speed using Automatic Weather Station", The Transactions of the Korean Institute of Electrical Engineers, vol. 64, No.1, pp. 107-112, 2015 https://doi.org/10.5370/KIEE.2015.64.1.107
  17. D. Zongker and B. Punch : Lil-GP User's Manual, Michigan State University, 1995.
  18. D. Kim, and K. Seo, "Comparison of Linear and Nonlinear Regressions and Elements Analysis for Wind Speed Prediction", Journal of Korean Institute of Intelligent Systems, Vol. 25, No. 5, pp. 477-482, 2015 https://doi.org/10.5391/JKIIS.2015.25.5.477