Abstract
We present the first large real-world application for the neural network optimizing genetic algorithm Enzo. Nets had several thousands links and the training data up to over 200,000 patterns. We evolved nets for a classification task that have an order of magnitude free parameters less than commonly used polynomial classifiers while maintaining the same performance.
To achieve this we implemented some significant enhancements and minor improvements of the original algorithm.
It is also shown how to use Enzo as an efficient tool to create nets satisfying task-specific constraints.
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References
Heinrich Braun and Joachim Weisbrod. Evolving feedforward neural networks. Proceedings of the International Conference on Artificial Neural Nets and Genetic Algorithms, 1993.
Heinrich Braun and Peter Zagorski. Enzo-M: A hybrid approach for optimizing neural networks by evolution and learning. In Proceedings of the third parallel problem solving from Nature, Jerusalem, Israel, 1994.
R. Allan Wilkinson et al The first census optical character recognition systems conference. NIST ir4912. Available at the NIST-Archive: sequoya.ucsl.nist.gov.
John McDonell and Don Waagen. Neural structure design by evolutionary programming, 1993. NCCOSC, RDT & E Division, San Diego, CA 92152.
Martin Riedmiller and Heinrich Braun. A direct adaptive method for faster backpropagation learning: The Rprop algorithm. In Proceedings of the ICNN 93, San Francisco, 1993.
D.E. Rumelhart, G. Hinton, and R. Williams. Learning internal representations by error propagation. In D.E. Rumelhart and J.L. McClelland, editors, Parallel Distributed Processing, Vol. I Foundations, pages 318–362. MIT Press, Cambridge, MA, 1986.
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© 1995 Springer-Verlag/Wien
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Schäfer, J., Braun, H. (1995). Optimizing Classifiers for Handwritten Digits by Genetic Algorithms. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_5
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DOI: https://doi.org/10.1007/978-3-7091-7535-4_5
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82692-8
Online ISBN: 978-3-7091-7535-4
eBook Packages: Springer Book Archive