Parallel Growing SOM Monitored by Genetic Algorithm

Genetic algorithms are an effective search technique to utilize when the search space of a problem is very large and an unintelligent brute-force search is too time-consuming. One such problem that would benefit from a genetic algorithm is the optimization of the ParaGSOM, a Self-Organizing Map that processes the input space in parallel. The ParaGSOM has several parameters that can be configured with a wide range of possible values. Each of these parameters can significantly change the behavior of the ParaGSOM, depending on the value. These behavioral changes will affect the ParaGSOM´s ability to adapt to the input space, leading to anything from a fast convergence to a slow convergence to no convergence at all. Applying a genetic algorithm to determine the optimal parameters to use for fast, accurate convergence in the ParaGSOM yields results much faster than testing each parameter combination individually. A genetic algorithm gives insight about how particular parameter combinations affect the network and shows how their relationships can be exploited for maximum efficiency of the ParaGSOM.