Optimizations of the Gravitationally Organized Related Mapping ANN through Genetic Algorithms

GORMANN is a self-organizing neural network utilizing Newton's law of universal gravitation. The results GORMANN are similar to a cross between a Kohonen SOM and morphological skeleton: the input is discretized and only the key features of the input are preserved. Like the SOM, GORMANN requires a number of input parameters (e.g. the learning rate) which greatly impact the quality of the resulting network. There have been several successful applications of genetic algorithms to optimize SOM parameters. Based on the success of these efforts, we applied a genetic algorithm to GORMANN to achieve the same end. Due to the theoretical nature and immaturity of the GORMANN algorithm, especially when compared to the Kohonen SOM, a group of unique challenges must be overcome. As GORMANN is a young neural network architecture, it has no strong theoretical background. In addition, the uniqueness of GORMANN requires new methods of measuring and comparing performance. In this paper we introduce a genetic algorithm-based method for optimizing the input parameters of GORMANN. A group of two-dimensional input patterns is used to illustrate the effectiveness of our method