Data-Driven Evolutionary Computation Under Continuously Streaming Environments: A Drift-Aware Approach

Streaming Data-Driven Evolutionary Algorithms (SDDEAs) have emerged as a crucial paradigm in the area of data-driven optimization. However, current methods face critical limitations when handling unpredictable concept drifts in continuously evolving environments. To address this research gap, we propose DASE, a drift-aware streaming evolutionary algorithm that features two key innovations. First, we introduce a hierarchical confidence drift detector that operates on a moving window over continuous data streams, identifying concept drifts by evaluating statistical deviations in model accuracy. Second, we propose a context-aware warm start mechanism that adaptively transfers knowledge from historical environments to the new environment using environmental similarity-based weighting. These dual innovations not only enables automatic segmentation of streaming data into coherence environments but also enhances optimization performance with the real-time responsiveness. Experimental evaluations on benchmark problems demonstrate that DASE significantly outperforms state-of-the-art algorithms across various drift scenarios, establishing it as a powerful method for addressing challenges in continuously streaming environment.