# Adaptation Strategies for Automated Machine Learning on Evolving Data

arXiv: https://arxiv.org/abs/2006.06480 · HTML: https://mlsanity.com/paper/2006.06480/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2020.

## Built on by (most cited first, 12 of 12)

- [Automated Deep Learning: Neural Architecture Search Is Not the End](https://mlsanity.com/paper/2112.09245/) (2021)
- [Online AutoML: An adaptive AutoML framework for online learning](https://mlsanity.com/paper/2201.09750/) (2022)
- [Generalized Incremental Learning under Concept Drift across Evolving Data Streams](https://mlsanity.com/paper/2506.05736/) (2025)
- [Concepts for Automated Machine Learning in Smart Grid Applications](https://mlsanity.com/paper/2110.13585/) (2021)
- [Wind turbine condition monitoring based on intra- and inter-farm federated learning](https://mlsanity.com/paper/2409.03672/) (2024)
- [Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting](https://mlsanity.com/paper/2302.10347/) (2023)
- [Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences](https://mlsanity.com/paper/2403.19871/) (2024)
- [Sharpness-Aware Minimization for Evolutionary Feature Construction in Regression](https://mlsanity.com/paper/2405.06869/) (2024)
- [Pad'e Approximant Neural Networks for Enhanced Electric Motor Fault Diagnosis Using Vibration and Acoustic Data](https://mlsanity.com/paper/2507.02599/) (2025)
- [Robustness of AutoML on Dirty Categorical Data](https://mlsanity.com/paper/2602.00412/) (2026)
- [Systematic Evaluation of TabPFN-TS and Chronos-2 for Zero-Shot Heat Load Forecasting in District Heating Networks](https://mlsanity.com/paper/2608.20024/) (2026)
- [SCCM : Stream Cruise Control Method for Automated Drift Detection and Adaptation](https://mlsanity.com/paper/2609.09432/) (2026)

## Abstract (arXiv)

Automated Machine Learning (AutoML) systems have been shown to efficiently build good models for new datasets. However, it is often not clear how well they can adapt when the data evolves over time. The main goal of this study is to understand the effect of data stream challenges such as concept drift on the performance of AutoML methods, and which adaptation strategies can be employed to make them more robust. To that end, we propose 6 concept drift adaptation strategies and evaluate their effectiveness on different AutoML approaches. We do this for a variety of AutoML approaches for building machine learning pipelines, including those that leverage Bayesian optimization, genetic programming, and random search with automated stacking. These are evaluated empirically on real-world and synthetic data streams with different types of concept drift. Based on this analysis, we propose ways to develop more sophisticated and robust AutoML techniques.