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Reseach Article
Performance Analysis of Neural Networks Training using Real Coded Genetic Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 51 - Number 18 |
| Year of Publication: 2012 |
| Authors: Partha Pratim Sarangi, Banshidhar Majhi, Madhumita Panda |
10.5120/8143-1891
|
Partha Pratim Sarangi, Banshidhar Majhi, Madhumita Panda . Performance Analysis of Neural Networks Training using Real Coded Genetic Algorithm. International Journal of Computer Applications. 51, 18 ( August 2012), 30-36. DOI=10.5120/8143-1891
Abstract
Multilayer perceptrons (MLPs) are widely used for pattern classification and regression problems. Backpropagation (BP) algorithm is known technique in the training of multilayer perceptrons. However for its optimum training convergence, the learning and momentum parameters need to be tuned on trial and error method. Further, sometimes the backpropagation algorithm fails to achieve global convergence. To alleviate these problems we suggest a genetic algorithm based training for MLP network. Both binary coded and real coded genetic algorithm are used and a comparative training performance analysis has been studied. It is observed from simulation results that both the schemes outperform backpropagation algorithm and achieve global convergence. Further the real coded GA based training shows a faster convergence than binary coded GA based training. For simulation datasets are taken from UCI based machine learning repository. Hence real-coded genetic algorithm finds an alternative for back propagation based training algorithm.
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