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Comparing backpropagation with a genetic algorithm for neural network training

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  • Gupta, Jatinder N. D.
  • Sexton, Randall S.

Abstract

This article shows that the use of a genetic algorithm can provide better results for training a feedforward neural network than the traditional techniques of backpropagation. Using a chaotic time series as an illustration, we directly compare the genetic algorithm and backpropagation for effectiveness, ease-of-use, and efficiency for training neural networks.

Suggested Citation

  • Gupta, Jatinder N. D. & Sexton, Randall S., 1999. "Comparing backpropagation with a genetic algorithm for neural network training," Omega, Elsevier, vol. 27(6), pages 679-684, December.
    • Handle: RePEc:eee:jomega:v:27:y:1999:i:6:p:679-684
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    References listed on IDEAS

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    1. Baumol, William J & Benhabib, Jess, 1989. "Chaos: Significance, Mechanism, and Economic Applications," Journal of Economic Perspectives, American Economic Association, vol. 3(1), pages 77-105, Winter.
    2. Sexton, Randall S. & Dorsey, Robert E. & Johnson, John D., 1999. "Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing," European Journal of Operational Research, Elsevier, vol. 114(3), pages 589-601, May.
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    Cited by:

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    7. Jatinder N. D. Gupta & Randall S. Sexton & Enar A. Tunc, 2000. "Selecting Scheduling Heuristics Using Neural Networks," INFORMS Journal on Computing, INFORMS, vol. 12(2), pages 150-162, May.
    8. Curry, B. & Morgan, P. H., 2004. "Evaluating Kohonen's learning rule: An approach through genetic algorithms," European Journal of Operational Research, Elsevier, vol. 154(1), pages 191-205, April.
    9. B Dengiz & C Alabas-Uslu & O Dengiz, 2009. "A tabu search algorithm for the training of neural networks," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(2), pages 282-291, February.
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