Skip to main content

Neuron-Synapse Level Problem Decomposition Method for Cooperative Neuro-Evolution of Feedforward Networks for Time Series Prediction

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9491))

Included in the following conference series:

Abstract

A major concern in cooperative coevolution for neuro- evolution is the appropriate problem decomposition method that takes into account the architectural properties of the neural network. Decomposition to the synapse and neuron level has been proposed in the past that have their own strengths and limitations depending on the application problem. In this paper, a new problem decomposition method that combines neuron and synapse level is proposed for feedfoward networks and applied to time series prediction. The results show that the proposed approach has improved the results in selected benchmark data sets when compared to related methods. It also has promising performance when compared to other computational intelligence methods from the literature.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Institutional subscriptions

Similar content being viewed by others

References

  1. Potter, M., De Jong, K.: A cooperative coevolutionary approach to function optimization. In: Davidor, Y., Schwefel, H.-P., Mnner, R. (eds.) PPSN III. LNCS, vol. 866, pp. 249–257. Springer, Heidelberg (1994)

    Chapter  Google Scholar 

  2. Chandra, R., Frean, M.R., Zhang, M.: Crossover-based local search in cooperative co-evolutionary feedforward neural networks. Appl. Soft Comput. 12(9), 2924–2932 (2012)

    Article  Google Scholar 

  3. García-Pedrajas, N., Ortiz-Boyer, D.: A cooperative constructive method for neural networks for pattern recognition. Pattern Recogn. 40(1), 80–98 (2007)

    Article  MATH  Google Scholar 

  4. Lehman, J., Miikkulainen, R.: Neuroevolution. Scholarpedia 8(6), 30977 (2013)

    Article  Google Scholar 

  5. Potter, M.A., De Jong, K.A.: Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol. Comput. 8(1), 1–29 (2000)

    Article  Google Scholar 

  6. Chandra, R., Frean, M., Zhang, M.: On the issue of separability for problem decomposition in cooperative neuro-evolution. Neurocomputing 87, 33–40 (2012)

    Article  Google Scholar 

  7. Chandra, R.: Competitive two-island cooperative coevolution for training Elman recurrent networks for time series prediction. In: International Joint Conference on Neural Networks (IJCNN), Beijing, China, pp. 565–572, July 2014

    Google Scholar 

  8. Chandra, R.: Adaptive problem decomposition in cooperative coevolution of recurrent networks for time series prediction. In: International Joint Conference on Neural Networks (IJCNN), Dallas, TX, USA, pp. 1–8, August 2013

    Google Scholar 

  9. Chandra, R., Zhang, M.: Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction. Neurocomputing 186, 116–123 (2012)

    Article  Google Scholar 

  10. Gomez, F., Mikkulainen, R.: Incremental evolution of complex general behavior. Adapt. Behav. 5(3–4), 317–342 (1997)

    Article  Google Scholar 

  11. Gomez, F., Schmidhuber, J., Miikkulainen, R.: Accelerated neural evolution through cooperatively coevolved synapses. J. Mach. Learn. Res. 9, 937–965 (2008)

    MathSciNet  MATH  Google Scholar 

  12. Chandra, R., Frean, M., Zhang, M.: An encoding scheme for cooperative coevolutionary feedforward neural networks. In: Li, J. (ed.) AI 2010. LNCS, vol. 6464, pp. 253–262. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  13. Chandra, R.: Competition and collaboration in cooperative coevolution of Elman recurrent neural networks for time-series prediction. IEEE Trans. Neural Netw. Learn. Syst. (2015). (in press)

    Google Scholar 

  14. Chandra, R., Frean, M., Zhang, M., Omlin, C.W.: Encoding subcomponents in cooperative co-evolutionary recurrent neural networks. Neurocomputing 74(17), 3223–3234 (2011)

    Article  Google Scholar 

  15. Garcia-Pedrajas, N., Hervas-Martinez, C., Munoz-Perez, J.: COVNET: a cooperative coevolutionary model for evolving artificial neural networks. IEEE Trans. Neural Netw. 14(3), 575–596 (2003)

    Article  Google Scholar 

  16. Gomez, F.J.: Robust non-linear control through neuroevolution. Ph.D. Thesis, Department of Computer Science, The University of Texas at Austin, Technical Report AI-TR-03-303 (2003)

    Google Scholar 

  17. Takens, F.: Detecting strange attractors in turbulence. In: Rand, D., Young, L.-S. (eds.) Dynamical Systems and Turbulence, Warwick 1980. LNM, vol. 898, pp. 366–381. Springer, Heidelberg (1981)

    Chapter  Google Scholar 

  18. Chand, S., Chandra, R.: Cooperative coevolution of feed forward neural networks for financial time series problem. In: International Joint Conference on Neural Networks (IJCNN), Beijing, China, pp. 202–209, July 2014

    Google Scholar 

  19. Mackey, M., Glass, L.: Oscillation and chaos in physiological control systems. Science 197(4300), 287–289 (1977)

    Article  Google Scholar 

  20. Lorenz, E.: Deterministic non-periodic flows. J. Atmos. Sci. 20, 267–285 (1963)

    Google Scholar 

  21. SILSO World Data Center, The International Sunspot Number (1834–2001), International Sunspot Number Monthly Bulletin and Online Catalogue, Royal Observatory of Belgium, Avenue Circulaire 3, 1180 Brussels, Belgium. http://www.sidc.be/silso/. Accessed 02 February 2015

  22. NASDAQ Exchange Daily: 1970–2010 Open, Close, High, Low and Volume. http://www.nasdaq.com/symbol/aciw/stock-chart. Accessed 02 February 2015

  23. Deb, K., Anand, A., Joshi, D.: A computationally efficient evolutionary algorithm for real-parameter optimization. Evol. Comput. 10(4), 371–395 (2002)

    Article  Google Scholar 

  24. Gholipour, A., Araabi, B.N., Lucas, C.: Predicting chaotic time series using neural and neurofuzzy models: a comparative study. Neural Process. Lett. 24, 217–239 (2006)

    Article  Google Scholar 

  25. Chand, S., Chandra, R.: Multi-objective cooperative coevolution of neural networks for time series prediction. In: International Joint Conference on Neural Networks (IJCNN), Beijing, China, pp. 190–197, July 2014

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ravneil Nand .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Nand, R., Chandra, R. (2015). Neuron-Synapse Level Problem Decomposition Method for Cooperative Neuro-Evolution of Feedforward Networks for Time Series Prediction. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-26555-1_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26554-4

  • Online ISBN: 978-3-319-26555-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics