Competitive two-island cooperative coevolution for training Elman recurrent networks for time series prediction

Problem decomposition is an important aspect in using cooperative coevolution for neuro-evolution. Cooperative coevolution employs different problem decomposition methods to decompose the neural network training problem into subcomponents. Different problem decomposition methods have features that are helpful at different stages in the evolutionary process. Adaptation, collaboration and competition are characteristics that are needed for cooperative coevolution as multiple sub-populations are used to represent the problem. It is important to add collaboration and competition in cooperative coevolution. This paper presents a competitive two-island cooperative coevolution method for training recurrent neural networks on chaotic time series problems. Neural level and Synapse level problem decomposition is used in each of the islands. The results show improvement in performance when compared to standalone cooperative coevolution and other methods from literature.