Genetic Algorithm Based K-Means Fast Learning Artificial Neural Network

The K-means Fast Learning Artificial Neural Network (KFLANN) is a small neural network bearing two types of parameters, the tolerance, δ and the vigilance, μ. In previous papers, it was shown that the KFLANN was capable of fast and accurate assimilation of data [12]. However, it was still an unsolved issue to determine the suitable values for δ and μ in [12]. This paper continues to follows-up by introducing Genetic Algorithms as a possible solution for searching through the parameter space to effectively and efficiently extract suitable values to δ and μ. It is also able to determine significant factors that help achieve accurate clustering. Experimental results are presented to illustrate the hybrid GA-KFLANN ability using available test data.