Accurate prediction of stock market behavior is a challenging issue for financial forecasting. Artificial neural networks, such as multilayer perceptron have been established as better approximation and classification models for this domain. This study proposes a chemical reaction optimization...

This paper presents an optimization approach-residual-based bootstrap averaging (RBBA)-for different types of forecast ensembles. Unlike traditional residual-mean-square-error-based ensemble forecast averaging approaches, the RBBA method attempts to find optimal forecast weights in an ensemble...

This study attempts to accelerate the learning ability of an artifcial electric feld algorithm (AEFA) by attributing it with two mechanisms: elitism and oppositionbased learning. Elitism advances the convergence of the AEFA towards global optima by retaining the fne-tuned solutions obtained thus...