Hybrid Approach Based on Combination of Backpropagation and Evolutionary Algorithms for Artificial Neural Networks Training by Using Mobile Devices in Distributed Computing Environment

When Evolutionary Algorithms (EAs) are used for Artificial Neural Networks (ANNs) training, the most valuable advantage is the potential for this training to be done in parallel or even using distributed computing. With the capabilities of modern mobile devices, for example their use for distributed computations, they can be used much more extensively for scientific calculations. It is well known that distributed computing systems are limited by their communication bandwidth, because of network latency. In such environment some EAs are pretty suitable for distributed implementation. This is because of their high level of parallelism and relatively less intensive network communication needs. Subset of distributed computing is volunteer computing where users donate some of the computing power provided by devices under their control. This research proposes Android Live Wallpaper volunteer computing implementation of a system used for financial time series prediction. The forecasting module is organized as ANN, which is trained by hybrid combination of Backpropagation and EAs.