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

  • Ravneil NandEmail author
  • Rohitash Chandra
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9491)


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.


  1. 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)CrossRefGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 3.
    García-Pedrajas, N., Ortiz-Boyer, D.: A cooperative constructive method for neural networks for pattern recognition. Pattern Recogn. 40(1), 80–98 (2007)CrossRefzbMATHGoogle Scholar
  4. 4.
    Lehman, J., Miikkulainen, R.: Neuroevolution. Scholarpedia 8(6), 30977 (2013)CrossRefGoogle Scholar
  5. 5.
    Potter, M.A., De Jong, K.A.: Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol. Comput. 8(1), 1–29 (2000)CrossRefGoogle Scholar
  6. 6.
    Chandra, R., Frean, M., Zhang, M.: On the issue of separability for problem decomposition in cooperative neuro-evolution. Neurocomputing 87, 33–40 (2012)CrossRefGoogle Scholar
  7. 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 2014Google Scholar
  8. 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 2013Google Scholar
  9. 9.
    Chandra, R., Zhang, M.: Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction. Neurocomputing 186, 116–123 (2012)CrossRefGoogle Scholar
  10. 10.
    Gomez, F., Mikkulainen, R.: Incremental evolution of complex general behavior. Adapt. Behav. 5(3–4), 317–342 (1997)CrossRefGoogle Scholar
  11. 11.
    Gomez, F., Schmidhuber, J., Miikkulainen, R.: Accelerated neural evolution through cooperatively coevolved synapses. J. Mach. Learn. Res. 9, 937–965 (2008)MathSciNetzbMATHGoogle Scholar
  12. 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) CrossRefGoogle Scholar
  13. 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. 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)CrossRefGoogle Scholar
  15. 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)CrossRefGoogle Scholar
  16. 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. 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)CrossRefGoogle Scholar
  18. 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 2014Google Scholar
  19. 19.
    Mackey, M., Glass, L.: Oscillation and chaos in physiological control systems. Science 197(4300), 287–289 (1977)CrossRefGoogle Scholar
  20. 20.
    Lorenz, E.: Deterministic non-periodic flows. J. Atmos. Sci. 20, 267–285 (1963)Google Scholar
  21. 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. Accessed 02 February 2015
  22. 22.
    NASDAQ Exchange Daily: 1970–2010 Open, Close, High, Low and Volume. Accessed 02 February 2015
  23. 23.
    Deb, K., Anand, A., Joshi, D.: A computationally efficient evolutionary algorithm for real-parameter optimization. Evol. Comput. 10(4), 371–395 (2002)CrossRefGoogle Scholar
  24. 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)CrossRefGoogle Scholar
  25. 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 2014Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Computing Information and Mathematical SciencesUniversity of South PacificSuvaFiji
  2. 2.Artificial Intelligence and Cybernetics Research GroupSoftware FoundationNausoriFiji

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