Genetic Synthesis of Unsupervised Learning Algorithms

This paper presents new unsupervised learning algorithms that have been synthesized using a genetic approach. A set of such learning algorithms has been compared with the classical Kohonen's Algorithm on the Self-Organizing Map and has been found to provide a better performance measure. This study indicates that there exist many unsupervised learning algorithms that lead to an organization similar to that of Kohonen's Algorithm, and that genetic algorithms can be used to search for optimal algorithms and optimal architectures for the unsupervised learning.