Evolving Machine Learning: A Survey

2025-05-23

AI Summary

This paper addresses five core challenges in dynamic data environments—data drift, concept drift, catastrophic forgetting, skewed learning, and network adaptability. Method: It systematically surveys over 120 state-of-the-art evolutionary machine learning (EML) works, integrating online learning, incremental learning, continual learning, meta-learning, dynamic pruning, and ensemble distillation to establish a multi-paradigm evaluation framework covering supervised, unsupervised, and semi-supervised settings. Contribution/Results: The work introduces the first unified analytical framework for EML, clarifies challenge taxonomies, uncovers synergistic mechanisms among adaptive neural architectures, meta-learning, and ensemble strategies, and identifies critical gaps in robustness, ethics, and scalability. It delivers a comprehensive EML methodology landscape, a curated collection of mainstream benchmarks and evaluation metrics, and system design principles tailored for industrial deployment—providing both theoretical foundations and practical guidance for building dynamic AI systems.

Technology Category

• Machine Learning: Evolutionary Learning
• Multiagent Systems: Multiagent Learning
• Search and Optimization: Evolutionary Computation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In an era defined by rapid data evolution, traditional machine learning (ML) models often fall short in adapting to dynamic environments. Evolving Machine Learning (EML) has emerged as a critical paradigm, enabling continuous learning and adaptation in real-time data streams. This survey presents a comprehensive analysis of EML, focusing on five core challenges: data drift, concept drift, catastrophic forgetting, skewed learning, and network adaptation. We systematically review over 120 studies, categorizing state-of-the-art methods across supervised, unsupervised, and semi-supervised approaches. The survey explores diverse evaluation metrics, benchmark datasets, and real-world applications, offering a comparative lens on the effectiveness and limitations of current techniques. Additionally, we highlight the growing role of adaptive neural architectures, meta-learning, and ensemble strategies in addressing evolving data complexities. By synthesizing insights from recent literature, this work not only maps the current landscape of EML but also identifies critical gaps and opportunities for future research. Our findings aim to guide researchers and practitioners in developing robust, ethical, and scalable EML systems for real-world deployment.

Problem

• Addressing adaptation challenges in dynamic data environments
• Analyzing core EML issues like data and concept drift
• Exploring adaptive methods for real-time learning systems

Innovation

• Adaptive neural architectures for dynamic data
• Meta-learning for continuous real-time adaptation
• Ensemble strategies to handle data drift