NDLI: Training Feed-Forward Neural Networks Using a Parallel Genetic Algorithm with the Best Must Survive Strategy

Content Provider	IEEE Xplore Digital Library
Author	Kattan, A.R.M. Abdullah, R. Salam, R.A.
Copyright Year	2010
Abstract	Feed-Forward Artificial Neural Networks (FFANN) can be trained using Genetic Algorithm (GA). GA offers a stochastic global optimization technique that might suffer from two major shortcomings: slow convergence time and impractical data representation. The effect of these shortcomings is more considerable in case of larger FFANN with larger dataset. Using a non-binary real-coded data representation we offer an enhancement to the generational GA used for the training of FFANN. Such enhancement would come in two fold: The first being a new strategy to process the strings of the population by allowing the fittest string to survive unchanged to the next population depending on its age. The second is to speed up fitness computation time through the utilization of known parallel processing techniques used for matrix multiplication. The implementation was carried on master-slaves architecture of commodity computers connected via Ethernet. Using a well-known benchmarking dataset, results show that our proposed technique is superior to the standard in terms of both the overall convergence time and processing time.
Starting Page	96
Ending Page	99
File Size	856656
Page Count	4
File Format	PDF
ISBN	9781424459841
e-ISBN	9781424459858
DOI	10.1109/ISMS.2010.29
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-01-27
Publisher Place	United Kingdom
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Concurrent computing Neural networks Stochastic processes Artificial neural networks Computer architecture Parallel processing Matrix-multiplication Feedforward neural networks Neural network Feedforward systems Genetic algorithms Convergence
Content Type	Text
Resource Type	Article

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