Skip to main content
SpringerLink
Log in
Book cover

Hybrid Self-Organizing Modeling Systems pp 27–98Cite as

Hybrid Genetic Programming and GMDH System: STROGANOFF

  • Chapter

  • 657 Accesses

Part of the book series: Studies in Computational Intelligence ((SCI,volume 211))

Abstract

This chapter introduces a new approach to Genetic Programming (GP), based on GMDH-based technique, which integrates a GP-based adaptive search of tree structures, and a local parameter tuning mechanism employing statistical search. The GP is supplemented with a local hill climbing search, using a parameter tuning procedure. More precisely, we integrate the structural search of traditional GP with a multiple regression analysis method and establish our adaptive program called .STROGANOFF’ (i.e. STructured Representation On Genetic Algorithms for NOnlinear Function Fitting). The fitness evaluation is based on aMinimumDescription Length (MDL) criterion, which effectively controls the tree growth in GP. Its effectiveness is demonstrated by solving several system identification (numerical) problems and comparinf the performance of STROGANOFF with traditional GP and another standard technique. The effectiveness of this numerical approach to GP is demonstrated by successful application to computational finances.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Angeline, P.J., Saunders, G.M., Pollack, J.B.: An Evolutionary Algorithm that Constructs Recurrent Neural Networks. IEEE Tr. Neural Networks 5(1) (January 1994)

    Google Scholar 

  2. Angeline, P.: Two Self-Adaptive Crossover Operators for Genetic Programming. In: Angeline, P., Kinnear, K. (eds.) Advances in Genetic Programming 2. MIT Press, Cambridge (1996)

    Google Scholar 

  3. Aranha, C., Kasai, O., Uchide, U., Iba, H.: Day-Trading Rules Development by Genetic Programming. In: Proc. 6th International Conference on Computational Intelligence in Economics & Finance (CIEF), pp. 515–521 (2007)

    Google Scholar 

  4. Armstrong, W.W., Gecsei, J.: Adaptation Algorithms for Binary Tree Networks. IEEE TR. SMC SMC-9(5) (1979)

    Google Scholar 

  5. Armstrong, W.W.: Learning and Generalization in Adaptive Logic Networks. In: Kohonen, T. (ed.) Artificial Neural Networks, pp. 1173–1176. Elsevier Science Pub., Amsterdam (1991)

    Google Scholar 

  6. Astrom, K.J., Eykhoff, P.: System Identification, a survey. Automatica 7, 123–162 (1971)

    Article  MathSciNet  Google Scholar 

  7. Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming: An Introduction. In: On the Automatic Evolution of Computer Programs and Its Applications. Morgan Kaufmann, San Francisco (1998)

    Google Scholar 

  8. Barzdins, J.M., Barzdins, G.J.: Rapid Construction of Algebraic Axioms from Samples. Theoretical Computer Science 90, 179–208 (1991)

    MathSciNet  Google Scholar 

  9. Belew, R.K., McInerney, J., Schraudolph, N.N.: Evolving Networks: Using Genetic Algorithm with Connectionist Learning. In: Langton, C.G., et al. (eds.) Artificial Life II. Addison-Wesley, Reading (1991)

    Google Scholar 

  10. Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)

    Google Scholar 

  11. Box, G.E.P., Jenkins, G.M.: Time Series Analysis Forecasting and Control, Holden-Day, San Francisco, CA (1970)

    Google Scholar 

  12. Chidambaran, N.K., Lee, C.H.J., Trigueros, J.R.: An Adaptive Evolutionary Approach to Option Pricing via Genetic Programming. In: Proc. of the 3rd Annual Genetic Programming Conference (1998)

    Google Scholar 

  13. de Menezes, L., Nikolaev, N.: Forecasting with Genetically Programmed Polynomial Neural Networks. Int. J. of Forecasting (2006)

    Google Scholar 

  14. Farlow, S.J. (ed.): Self-Organizing Methods in Modeling, GMDH Type Algorithms. Marcel Dekker, Inc., New York (1984)

    MATH  Google Scholar 

  15. Faraway, J., Chatfield, C.: Time Series Forecasting with Neural Networks: A Comparative Study using the Airline Data. Applied Statistics 47(2), 231–250 (1998)

    Google Scholar 

  16. Fogel, D.B.: Evolving Behaviors in the Iterated Prisoner’s Dilemma. Evolutionary Computation 1(1) (1993)

    Google Scholar 

  17. Franke, R.: Scattered Data Interpolation: Tests of Some Methods. Math. Comp. 38, 181–200 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  18. Giles, C.L., Miller, C.B., Chen, D., Chen, H.H., Sun, G.Z., Lee, Y.C.: Learning and Extracting Finite State Automata with Second-Order Recurrent Neural Networks. Neural Computation 4 (1992)

    Google Scholar 

  19. Hiemstra, Y.: Applying Neural Networks and Genetic Algorithms to Tactical Asset Allocation. Neuro Ve$t Journal (May/June 1996)

    Google Scholar 

  20. Hübner, U., Weiss, C.-O., Abraham, N.B., Tang, D.: Lorenz-Like Chaos in NH3-FIR Lasers. In: Weigend, A.S., Gershenfeld, N.A. (eds.) Time Series Prediction: Forecasting the Future and Understanding the Past, pp. 73–104. Addison-Wesley, Reading (1994)

    Google Scholar 

  21. Iba, H., Kurita, T., degaris, H., Sato, T.: System Identification using Structured Genetic Algorithms. in. In: Proc. of 5th International Joint Conference on Genetic Algorithms, pp. 279–286 (1993)

    Google Scholar 

  22. Iba, H., degaris, H., Sato, T.: Genetic Programming using a Minimum Description Length Principle. In: Kinnear Jr., K.E. (ed.) Advances in Genetic Programming, pp. 265–284. MIT Press, Cambridge (1994)

    Google Scholar 

  23. Iba, H., Sato, T.: Genetic Programming with Local Hill-Climbing. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 302–411. Springer, Heidelberg (1994)

    Google Scholar 

  24. Iba, H., deGaris, H., Sato, T.: System Identification Approach to Genetic Programming. In: Proc. of IEEE World Congress on Computational Intelligence, pp. 401–406. IEEE Press, Los Alamitos (1994)

    Chapter  Google Scholar 

  25. Iba, H., deGaris, H., Sato, T.: Temporal Data Processing Using Genetic Programming. In: Proc. of 6th International Conference on Genetic Algorithms, pp. 279–286 (1995)

    Google Scholar 

  26. Iba, H., deGaris, H.: Numerical Approach to Genetic Programming for System Identification Evolutionary Computation 3(4), 417–452 (1996)

    Google Scholar 

  27. Iba, H., deGaris, H.: Extending Genetic Programming with Recombinative Guidance. In: Angeline, P., Kinnear, K. (eds.) Advances in Genetic Programming 2. MIT Press, Cambridge (1996)

    Google Scholar 

  28. Ikeda, K.: Multiple-valued Stationary State and its Instability of the Transmitted Light by a Ring Cavity System. Opt. Commun. 30, 257–261 (1979)

    Article  Google Scholar 

  29. Ivakhnenko, A.G.: Polynomial Theory of Complex Systems. IEEE Tr. SMC SMC-1(4) (1971)

    Google Scholar 

  30. Janikow, C.Z.: A Knowledge-Intensive Genetic Algorithm for Supervised Learning. Machine Learning 13 (1993)

    Google Scholar 

  31. Kitano, H.: Designing Neural Networks using Genetic Algorithms with Graph Generation System. Complex Systems 4 (1990)

    Google Scholar 

  32. Koza, J.: Genetic programming: A paradigm for genetically breeding populations of computer programs to solve problems, Report No. STAN-CS-90-1314, Dept. of Computer Science, Stanford Univ. (1990)

    Google Scholar 

  33. Koza, J.: Genetic Programming, On the Programming of Computers by means of Natural Selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  34. Koza, J.: Genetic Programming II: Automatic Discovery of Reusable Subprograms. MIT Press, Cambridge (1994)

    Google Scholar 

  35. Kutza, K.: Neural Networks at Your Fingertips (1996), http://www.geocities.com/CapeCanaveral/1624/

  36. Langley, P., Zytkow, J.M.: Data-driven Approaches to Empirical Discovery. Artificial Intelligence 40, 283–312 (1989)

    Article  Google Scholar 

  37. Lorenz, E.N.: Deterministic Non-Periodic Flow. J. Atoms. Sci. 20, 130 (1963)

    Article  Google Scholar 

  38. Mackey, M.C., Glass, L.: Oscillation and Chaos in Physiological Control Systems. Science 197, 287–107 (1977)

    Google Scholar 

  39. MacKay, D.J.C.: Probable Networks and Plausible Predictions- A Review of Practical Bayesian Methods for Supervised Neural Networks. Network: Computation in Neural Systems 6(3), 469–505 (1995)

    Article  MATH  Google Scholar 

  40. MacDonnell, J.R., Waagen, D.: Evolving Recurrent Perceptrons for Time-Series Modeling. IEEE Tr. Neural Networks 5(1) (January 1994)

    Google Scholar 

  41. Nikolaev, N., Iba, H.: Adaptive Learning of Polynomial Networks Genetic Programming. In: Backpropagation and Bayesian Methods. Series: Genetic and Evolutionary Computation. Springer, Heidelberg (2006)

    Google Scholar 

  42. Oakley, H.: Two Scientific Applications of Genetic Programming: Stack Filters and Non-Linear Equation Fitting to Chaotic Data. In: Kinnear Jr., K.E. (ed.) Advances in Genetic Programming, pp. 369–389. MIT Press, Cambridge (1994)

    Google Scholar 

  43. Poggio, T., Girosi, F.: Networks for Approximation and Learning. Proc. of the IEEE 78(9), 1481–1497 (1990)

    Article  Google Scholar 

  44. Potvin, J.-Y., Soriano, P., Vallee, M.: Generating trading rules on the stock markets. Computer & Operations Research 31, 1033–1047 (2004)

    Article  MATH  Google Scholar 

  45. Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical Recipes in C, Cambridge (1988)

    Google Scholar 

  46. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Internal Representations by Error Propagation. In: Rumelhart, D.E., et al. (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, pp. 318–362. The MIT Press, Cambridge (1986)

    Google Scholar 

  47. Schaffer, J.D., Morishima, A.: An Adaptive Crossover Distribution Mechanism for Genetic Algorithms. In: Proc. of 2nd International Joint Conference on Genetic Algorithms, pp. 36–40. Lawrence Erlbaum, Mahwah (1987)

    Google Scholar 

  48. Spiegel, M.R.: Theory and Problems of Statistics. McGraw-Hill, New York (1975)

    Google Scholar 

  49. Sun, G.Z., Chen, H.H., Giles, C.L., Lee, Y.C., Chen, D.: Connectionist Pushdown Automata that Learn Context-Free Grammars. In: IJCNN 1990 WASH D.C. Lawrence Erlbaum, Mahwah (1990)

    Google Scholar 

  50. Teller, A., Veloso, M.: PADO: A New Learning Architecture for Object Recognition. In: Ikeuchi, K., Veloso, M. (eds.) Symbolic Visual Learning, pp. 81–116. Oxford University Press, Oxford (1996)

    Google Scholar 

  51. Tenorio, M.F., Lee, W.: Self-organizing Network for Optimum Supervised Learning. IEEE Tr. Neural Networks 1(1), 100–109 (1990)

    Article  Google Scholar 

  52. Tomata, M.: Dynamic Construction of Finite Automata from Examples using Hill-Climbing. In: Proc. 4th International Cognitive Science Conference (1982)

    Google Scholar 

  53. Watrous, R.L., Kuhn, G.M.: Induction of Finite-State Languages using Second-Order Recurrent Networks. Neural Computation 4 (1992)

    Google Scholar 

  54. Williams, R.J., Zipser, D.: Experimental Analysis of the Real-Time Recurrent Learning Algorithm. Connection Science 1(1) (1989)

    Google Scholar 

  55. Wray, J., Green, G.G.R.: Calculation of the Volterra Kernels of Non-linear Dynamic Systems using an Artificial Neural Networks. Biological Cybernetics 71(3), 187–195 (1994)

    Article  MATH  Google Scholar 

  56. Zhang, B.T., Mühlenbein, H.: Genetic Programming of Minimal Neural Networks using Occam’s Razor. In: Proc. of 5th International Joint Conference on Genetic Algorithms (1993)

    Google Scholar 

  57. Zhang, B.-T., Mühlenbein, H.: Balancing Accuracy and Parsimony in Genetic Programming. Evolutionary Computation 3(1), 17–38 (1995)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Engineering, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan

    Iba Hitoshi

Authors
  1. Iba Hitoshi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Knowledge Management & Mining Inc., Richmond Hill, Ontario, Canada

    Godfrey C. Onwubolu

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Hitoshi, I. (2009). Hybrid Genetic Programming and GMDH System: STROGANOFF. In: Onwubolu, G.C. (eds) Hybrid Self-Organizing Modeling Systems. Studies in Computational Intelligence, vol 211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01530-4_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-01530-4_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01529-8

  • Online ISBN: 978-3-642-01530-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation