GeM-EA: A Generative and Meta-learning Enhanced Evolutionary Algorithm for Streaming Data-Driven Optimization

2026-04-14

AI Summary

This work addresses the challenge of adapting to non-stationary environments in streaming data-driven optimization, where concept drift undermines model stability and performance. To tackle this issue, the authors propose a multi-island evolutionary framework that integrates meta-learning with generative replay. The approach employs a bi-level meta-learning strategy to rapidly initialize surrogate models and incorporates a linear residual component to enhance dynamic modeling capabilities. Concurrently, a generative replay mechanism preserves and reuses historical knowledge, enabling efficient knowledge transfer and robust optimization upon detecting concept drift. Experimental results on standard SDDO benchmarks demonstrate that the proposed method significantly outperforms existing techniques in both adaptation speed and optimization stability.

Technology Category

• Search and Optimization: Evolutionary Computation
• Machine Learning: Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

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 Concept drift produces non-stationary landscapes, making optimization methods challenging due to outdated models.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.

Problem

• Streaming Data-Driven Optimization
• Concept Drift
• Non-stationary Optimization
• Evolutionary Algorithm
• Surrogate Modeling

Innovation

• meta-learning
• generative replay
• evolutionary algorithm
• concept drift
• surrogate modeling