Evaluating Kohonen's learning rule: an approach through genetic algorithms

Curry, Bruce and Morgan, Peter Huw ORCID: https://orcid.org/0000-0002-8555-3493 2004. Evaluating Kohonen's learning rule: an approach through genetic algorithms. European Journal of Operational Research 154 (1) , pp. 191-205. 10.1016/S0377-2217(02)00643-4

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Abstract

This paper examines the technical foundations of the self-organising map (SOM). It compares Kohonen’s heuristic-based training algorithm with direct optimisation of a locally-weighted distortion index, also used by Kohonen. Direct optimisation is achieved through a genetic algorithm (GA). Although GAs have been used before with the SOM, this has not been done in conjunction with the distortion index. Comparing heuristic-based training and direct optimisation for the SOM is analogous to comparing the Backpropagation algorithm for feedforward networks with direct optimisation of RMS error. Our experiments reveal lower values of the distortion index with direct optimisation. As to whether the heuristic-based algorithm is able to provide an approximation to gradient descent, our results suggest the answer should be in the negative. Theorems for one-dimensional and for square maps indicate that different point densities will emerge for the two training approaches. Our findings are in accordance with these results.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Uncontrolled Keywords:	Neural network; Self-organising map; Distortion index; Genetic algorithm; Gradient descent
ISSN:	0377-2217
Last Modified:	17 Oct 2022 09:21
URI:	https://orca.cardiff.ac.uk/id/eprint/2829

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