A multi-objective micro genetic ELM algorithm

The extreme learning machine (ELM) is a methodology for learning single-hidden layer feedforward neural networks (SLFN) which has been proved to be extremely fast and to provide very good generalization performance. ELM works by randomly choosing the weights and biases of the hidden nodes and then analytically obtaining the output weights and biases for a SLFN with the number of hidden nodes previously fixed. In this work, we develop a multi-objective micro genetic ELM (μG-ELM)(μG-ELM) which provides the appropriate number of hidden nodes for the problem being solved as well as the weights and biases which minimize the MSE. The multi-objective algorithm is conducted by two criteria: the number of hidden nodes and the mean square error (MSE). Furthermore, as a novelty, μG-ELMμG-ELM incorporates a regression device in order to decide whether the number of hidden nodes of the individuals of the population should be increased or decreased or unchanged. In general, the proposed algorithm reaches better errors by also implying a smaller number of hidden nodes for the data sets and competitors considered.