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.
Please run the ''main_SDDObench.py''
If you find our DASE has given you some help, please cite it in your publications or projects:
@article{zhong2025data,
title={Data-Driven Evolutionary Computation Under Continuously Streaming Environments: A Drift-Aware Approach},
author={Zhong, Yuan-Ting and Gong, Yue-Jiao},
journal={IEEE Transactions on Evolutionary Computation},
year={2025},
publisher={IEEE}
}