Genetically optimized extreme learning machine

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.