TY  - JOUR
AU  - Ghosh, Ranadhir 
AU  - Yearwood, John 
AU  - Ghosh, Moumita 
AU  - Bagirov, Adil 
PY  - 2005
TI  - Hybridization of Neural Learning Algorithms Using Evolutionary and Discrete Gradient Approaches
JF  - Journal of Computer Science
VL  - 1
IS  - 3
DO  - 10.3844/jcssp.2005.387.394
UR  - https://thescipub.com/abstract/jcssp.2005.387.394
AB  - 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.