Adaptive problem decomposition in cooperative coevolution of recurrent networks for time series prediction

Abstract

Cooperative coevolution employs different problem decomposition methods to decompose the neural network problem into subcomponents. The efficiency of a problem decomposition method is dependent on the neural network architecture and the nature of the training problem. The adaptation of problem decomposition methods has been recently proposed which showed that different problem decomposition methods are needed at different phases in the evolutionary process. This paper employs an adaptive cooperative coevolution problem decomposition framework for training recurrent neural networks on chaotic time series problems. The Mackey Glass, Lorenz and Sunspot chaotic time series are used. The results show improvement in performance in most cases, however, there are some limitations when compared to cooperative coevolution and other methods from literature.