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Journal of Applied Sciences
  Year: 2013 | Volume: 13 | Issue: 2 | Page No.: 242-251
DOI: 10.3923/jas.2013.242.251
 
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A Robust Design Approach for GA-based Back Propagation Neural Networks Designed to Classify Data of Different Types
Chien-Yu Huang, I-Chiang Wang and Tien-Hui Chen

Abstract:
Genetic Algorithms (GAs) have a proven ability to improve the classification performance of Back-propagation Neural (BPN) networks by optimizing their topology and parameter settings. However, before they are used to optimize the BPN network, their parameters should be calibrated to improve the quality of the results. Accordingly, the current study develops a robust design method in which the Taguchi method is employed to establish appropriate values for the main GA parameters, namely the crossover rate, the mutation rate and the size of the population. The calibrated GA is used to optimize the parameters of BPN networks designed to classify three different types of data, continuous, ordinal and nominal, to immunize the noise of the data type. The classification performance of each GA-optimized BPN is verified using datasets downloaded from the server of the University of California’s Department of Information and Computer Science. The results demonstrate that the process of calibrating the GA’s parameters prior to its use in optimizing the BPN network yields a significant improvement in the network’s classification performance.
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How to cite this article:

Chien-Yu Huang, I-Chiang Wang and Tien-Hui Chen, 2013. A Robust Design Approach for GA-based Back Propagation Neural Networks Designed to Classify Data of Different Types. Journal of Applied Sciences, 13: 242-251.

DOI: 10.3923/jas.2013.242.251

URL: https://scialert.net/abstract/?doi=jas.2013.242.251

 
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