Enhancement of Ensemble Diversity Based on Grouping Genetic Algorithm and Its Application

To solve the problem of concept drift in complex industrial process and improve the generalization performance of the ensemble learning model, a modeling method was proposed to enhance diversity among base learners based on grouping genetic algorithm. The online sequential extreme learning machine (OS_ELM) was used as base learner. The base learners were grouped according to their performance on the sliding window, and evolution operations were performed. At the same time, the concept of gene flow was introduced, which increased the diversity among base learners and improved the prediction performance of the ensemble algorithm in dealing with the concept drift data streams. Finally, the rationality and effectiveness of the proposed algorithm were verified by using the synthetic data sets and real-world data sets.