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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)

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.

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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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