Genetic optimization of self-organizing feature maps

The authors present an application of the genetic algorithm to the design of Kohonen self-organizing feature maps. The genetic algorithm is used to optimize various parameters of the network model for a given problem. Performance criteria relevant to clustering or vector quantization applications are considered: root mean square error and an information-theoretic map entropy measure. Experimental results demonstrate the effectiveness of the approach, and suggest some interesting generalizations.<
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