Forecasting solar activity using co-evolution of models and tests

The cyclic solar activity has significant effects on earth, climate, satellites and space missions. Several methods have been introduced for the prediction of sunspot number, which is a common measure of solar activity. In this study a co-evolutionary algorithm is presented for inferring the topology and parameters of a multilayered neural network with the minimum of experimentation to the sunspot number time series which will be used as a predictor in predicting such phenomena. The algorithm synthesizes an explicit model directly from the observed data produced by intelligently generated tests. The algorithm is composed of two co-evolving populations; one population evolves candidate neural networks. The second population evolves informative tests that either extract new information from the hidden system or elicit desirable behavior from it. The fitness of candidate neural networks is their ability to explain behavior of the target chaotic system observed in response to all tests carried out so far; the fitness of candidate tests is their ability to make the models disagree in their predictions. The generality of this modeling-evaluation algorithm is demonstrated by applying the chosen model of this algorithm to predict sunspot number and the results depict the power of this training method which yields proper model to predict such chaotic time series.