Streaming Data-Driven Optimization (SDDO) problems arise in many applications where data arrive continuously and the optimization environment evolves over time. Concept drift produces non-stationary landscapes, making optimization methods challenging due to outdated models. Existing approaches often rely on simple surrogate combinations or directly injecting solutions, which may cause negative transfer under sudden environmental changes. We propose GeM-EA, a Generative and Meta-learning Enhanced Evolutionary Algorithm for SDDO that unifies meta-learned surrogate adaptation with generative replay for effective evolutionary search. Upon detecting concept drift, a bi-level meta-learning strategy rapidly initializes the surrogate using environment-relevant priors, while a linear residual component captures global trends. A multi-island evolutionary strategy further leverages historical knowledge via generative replay to accelerate optimization. Experimental results on benchmark SDDO problems demonstrate that GeM-EA achieves faster adaptation and improved robustness compared with state-of-the-art methods.

翻译：流式数据驱动优化（SDDO）问题广泛存在于数据持续到达且优化环境随时间演变的各类应用场景中。概念漂移会产生非平稳优化空间，使基于过时模型的优化方法面临挑战。现有方法通常依赖简单的替代模型组合或直接注入解向量，在环境突变时可能引发负迁移效应。本文提出GeM-EA——一种融合生成与元学习增强的SDDO进化算法，通过统一元学习替代模型自适应与生成式回放机制实现高效进化搜索。当检测到概念漂移时，双层元学习策略利用环境相关先验快速初始化替代模型，同时引入线性残差分量捕捉全局趋势；多岛屿进化策略借助生成式回放历史知识加速优化进程。在标准SDDO基准问题上的实验结果表明，与现有最优方法相比，GeM-EA实现了更快的适应速度和更强的鲁棒性。