Evolving RBF neural networks for time-series forecasting with EvRBF

This paper is focused on determining the parameters of radial basis function neural networks (number of neurons, and their respective centers and radii) automatically. While this task is often done by hand, or based in hillclimbing methods which are highly dependent on initial values, in this work, evolutionary algorithms are used to automatically build a radial basis function neural networks (RBF NN) that solves a specified problem, in this case related to currency exchange rates forecasting. The evolutionary algorithm EvRBF has been implemented using the evolutionary computation framework evolving object, which allows direct evolution of problem solutions. Thus no internal representation is needed, and specific solution domain knowledge can be used to construct specific evolutionary operators, as well as cost or fitness functions. Results obtained are compared with existent bibliography, showing an improvement over the published methods.

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