Genetically optimized extreme learning machine

Abstract

This paper proposes a learning algorithm for single-hidden layer feedforward neural networks (SLFN) called genetically optimized extreme learning machine (GO-ELM). In the GO-ELM, the structure and the parameters of the SLFN are optimized by a genetic algorithm (GA). The output weights, like in the batch ELM, are obtained by a least squares algorithm, but using Tikhonov's regularization in order to improve the SLFN performance in the presence of noisy data. The GA is used to tune the set of input variables, the hidden-layer configuration and bias, the input weights and the Tikhonov's regularization factor. The proposed method was applied and compared with four other methods over five benchmark problems available in a public repository. Besides it was applied in the estimation of the temperature at the burning zone of a real cement kiln plant.