Abstract
An approach is introduced for developing neural network pattern recognition systems using a hybrid evolutionary learning system for pattern recognition (HELPR) concept. A genetic algorithm is used to assemble detectors and pattern recognition systems while traditional weight training methods are used to determine weights. The results show that this novel approach develops simpler neural topologies than cascade correlation and can do so using very simple training metrics.
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© 1998 Springer-Verlag Berlin Heidelberg
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Wicker, D., Rizki, M.M., Tamburino, L.A. (1998). A hybrid evolutionary learning system for synthesizing neural network pattern recognition systems. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds) Evolutionary Programming VII. EP 1998. Lecture Notes in Computer Science, vol 1447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0040813
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DOI: https://doi.org/10.1007/BFb0040813
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