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Carnegie Mellon Robotics Institute

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Abstract

This paper aims to examine the use of genetic algorithms to optimize sub-systems of cellular neural network architectures. The application at hand is character recognition: the aim is to evolve an optimal feature detector in order to aid a conventional classifier network to generalize across different fonts. To this end, a performance function and a genetic encoding for a feature detector are presented. An experiment is described where an optimal feature detector is indeed found by the genetic algorithm.

Notes

Text Reference

Frank Dellaert and J. Vandewalle, "Automatic design of Cellular Neural Networks by means of Genetic Algorithms: Finding a Feature Detector," CNNA '94: proceedings, fourth International IEEE Workshop on Cellular Neural Networks and their Applications, 1994.

BibTeX Reference

@inproceedings{Dellaert_1994_898,
   author = "Frank Dellaert and J. Vandewalle",
   title = "Automatic design of Cellular Neural Networks by means of Genetic Algorithms: Finding a Feature Detector",
   booktitle = "CNNA '94: proceedings, fourth International IEEE Workshop on Cellular Neural Networks and their Applications",
   year = "1994",
}

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