An evolutionary extreme learning machine based on group search optimization

Extreme learning machine (ELM) was proposed as a new class of learning algorithm for single-hidden layer feedforward neural network (SLFN) much faster than the traditional gradient-based learning strategies. However, ELM random determination of the input weights and hidden biases may lead to non-optimal performance, and it might suffer from the overfitting as the learning model will approximate all training samples well. In this paper, a hybrid approach is proposed based on Group Search Optimizer (GSO) strategy to select input weights and hidden biases for ELM algorithm, called GSO-ELM. In addition, we evaluate the influence of different forms of handling members that fly out of the search space bounds. Experimental results show that GSO-ELM approach using different forms of dealing with out-bounded members is able to achieve better generalization performance than traditional ELM in real benchmark datasets.