Research Article Open Access

Hybridization of Neural Learning Algorithms Using Evolutionary and Discrete Gradient Approaches

Ranadhir Ghosh1, John Yearwood1, Moumita Ghosh1 and Adil Bagirov1
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Journal of Computer Science
Volume 1 No. 3, 2005, 387-394

DOI: https://doi.org/10.3844/jcssp.2005.387.394

Submitted On: 2 March 2005 Published On: 30 September 2005

How to Cite: Ghosh, R., Yearwood, J., Ghosh, M. & Bagirov, A. (2005). Hybridization of Neural Learning Algorithms Using Evolutionary and Discrete Gradient Approaches. Journal of Computer Science, 1(3), 387-394. https://doi.org/10.3844/jcssp.2005.387.394

Abstract

In this study we investigated a hybrid model based on the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. Also we discuss different variants for hybrid models using the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. The Discrete Gradient method has the advantage of being able to jump over many local minima and find very deep local minima. However, earlier research has shown that a good starting point for the discrete gradient method can improve the quality of the solution point. Evolutionary algorithms are best suited for global optimisation problems. Nevertheless they are cursed with longer training times and often unsuitable for real world application. For optimisation problems such as weight optimisation for ANNs in real world applications the dimensions are large and time complexity is critical. Hence the idea of a hybrid model can be a suitable option. In this study we propose different fusion strategies for hybrid models combining the evolutionary strategy with the discrete gradient method to obtain an optimal solution much quicker. Three different fusion strategies are discussed: a linear hybrid model, an iterative hybrid model and a restricted local search hybrid model. Comparative results on a range of standard datasets are provided for different fusion hybrid models.

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Keywords

  • Evolutionary Algorithm
  • Discrete Gradient