Multiobjective Evolution of Fuzzy Rough Neural Network via Distributed Parallelism for Stock Prediction

Fuzzy rough theory can describe real-world situations in a mathematically effective and interpretable way, while evolutionary neural networks can be utilized to solve complex problems. Combining them with these complementary capabilities may lead to evolutionary fuzzy rough neural network with the interpretability and prediction capability. In this article, we propose modifications to the existing models of fuzzy rough neural network and then develop a powerful evolutionary framework for fuzzy rough neural networks by inheriting the merits of both the aforementioned systems. We first introduce rough neurons and enhance the consequence nodes, and further integrate the interval type-2 fuzzy set into the existing fuzzy rough neural network model. Thus, several modified fuzzy rough neural network models are proposed. While simultaneously considering the objectives of prediction precision and network simplicity, each model is transformed into a multiobjective optimization problem by encoding the structure, membership functions, and the parameters of the network. To solve these optimization problems, distributed parallel multiobjective evolutionary algorithms are proposed. We enhance the optimization processes with several measures including optimizer replacement and parameter adaption. In the distributed parallel environment, the tedious and time-consuming neural network optimization can be alleviated by numerous computational resources, significantly reducing the computational time. Through experimental verification on complex stock time series prediction tasks, the proposed optimization algorithms and the modified fuzzy rough neural network models exhibit significant improvements the existing fuzzy rough neural network and the long short-term memory network.