Abstract
The Cellular Neural Network Universal Machine (CNN-UM) is a novel neuroprocessor algorithmically programmable having real time and supercomputer power implemented in a single VLSI chip. The local CNN connectivity provides an useful computation paradigm when the problem can be reformulated as a well-defined task where the signal values are placed on a regular 2-D grid (i.e., image processing), and the direct interaction between signal values are limited within a local neighborhood. This paper introduces a Genetic Programming technique to evolve both the structure and parameters of visual algorithms on this architecture. This is accomplished by defining a set of node functions and terminals to implement the basic operations commonly used. Lastly, the procedures involved in the use of the algorithm are illustrated by several applications.
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Preciado, V.M., Preciado, M.A., Jaramillo, M.A. (2004). Genetic Programming for Automatic Generation of Image Processing Algorithms on the CNN Neuroprocessing Architecture. In: Conejo, R., Urretavizcaya, M., Pérez-de-la-Cruz, JL. (eds) Current Topics in Artificial Intelligence. TTIA 2003. Lecture Notes in Computer Science(), vol 3040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25945-9_37
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DOI: https://doi.org/10.1007/978-3-540-25945-9_37
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