Abstract
A number of different methods combining the use of neural networks and genetic algorithms have been described [1]. This paper discusses an approach for training neural networks based on the geometric representation of the network. In doing so, the genetic algorithm becomes applicable as a common training method for a number of machine learning algorithms that can be similarly represented. The experiments described here were specifically derived to construct claim regions for Fuzzy ARTMAP Neural Networks [2],[3].
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J.T. Alander. An indexed bibliography of genetic algorithms and neural networks.
G.A. Carpenter et al. Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of multidimensional maps. IEEE Transactions on Neural Networks, 3(5):698–713., 1992.
Michael Georgiopoulos and Christos Christodoulou. Applications of Neural Networks in Electromagnetics. Artech House, 2001.
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© 2003 Springer-Verlag Berlin Heidelberg
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Holifield, G.A., Wu, A.S. (2003). A Genetic Algorithm as a Learning Method Based on Geometric Representations. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_38
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DOI: https://doi.org/10.1007/3-540-45110-2_38
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