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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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    1. McClelland, John W. & Wetzstein, Michael E. & Musser, Wesley N., 1986. "Returns To Scale And Size In Agricultural Economics," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 11(2), pages 1-5, December.
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    Cited by:

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    2. 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.
    3. 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.
    4. Montagno, Ray & Sexton, Randall S. & Smith, Brien N., 2002. "Using neural networks for identifying organizational improvement strategies," European Journal of Operational Research, Elsevier, vol. 142(2), pages 382-395, October.
    5. Ashwini Pradhan & Debahuti Mishra & Kaberi Das & Ganapati Panda & Sachin Kumar & Mikhail Zymbler, 2021. "On the Classification of MR Images Using “ELM-SSA” Coated Hybrid Model," Mathematics, MDPI, vol. 9(17), pages 1-21, August.
    6. Golmohammadi, Davood & Zhao, Lingyu & Dreyfus, David, 2023. "Using machine learning techniques to reduce uncertainty for outpatient appointment scheduling practices in outpatient clinics," Omega, Elsevier, vol. 120(C).
    7. Simsek, Serhat & Dag, Ali & Tiahrt, Thomas & Oztekin, Asil, 2021. "A Bayesian Belief Network-based probabilistic mechanism to determine patient no-show risk categories," Omega, Elsevier, vol. 100(C).
    8. Cinar, Didem & Kayakutlu, Gulgun & Daim, Tugrul, 2010. "Development of future energy scenarios with intelligent algorithms: Case of hydro in Turkey," Energy, Elsevier, vol. 35(4), pages 1724-1729.
    9. Xiaorui Shao & Chang-Soo Kim & Palash Sontakke, 2020. "Accurate Deep Model for Electricity Consumption Forecasting Using Multi-Channel and Multi-Scale Feature Fusion CNN–LSTM," Energies, MDPI, vol. 13(8), pages 1-22, April.
    10. 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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