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Descripción
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| This paper presents some connectionist models that are widely used to solve NP-problems. This paper shows some ideas about how to in- corporate a learning stage, based on self-organizing algorithms, in networks of evolutionary processors. T. Kohonen and P. Somervuo have shown that self or- ganizing maps (SOM) are not restricted to numerical data. This paper proposes a symbolic measure that is used to implement a string self organizing map based on SOM algorithm. Such measure between two strings is a new string. Compu- tation over strings is performed using a priority relationship among symbols, in this case, symbolic measure is able to generate new symbols. A complementary operation is defined in order to apply such measure to DNA strands. Finally, an algorithm is proposed in order to be able to implement a string self organizing map. This paper discusses the possibility of defining networks of evolutionary processors to rely on similarity instead of distance and shows examples of such networks for symbol strings. | |
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Internacional
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JCR del ISI
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Título de la revista
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Romanian Journal of Information Science and Technology |
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ISSN
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1453-8245 |
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Factor de impacto JCR
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0 |
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Información de impacto
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Volumen
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DOI
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Número de revista
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0 |
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