# Pulling the Carpet Below the Learner's Feet: Genetic Algorithm To Learn Ensemble Machine Learning Model During Concept Drift

arXiv: https://arxiv.org/abs/2412.09035 · HTML: https://mlsanity.com/paper/2412.09035/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 101 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Builds on (most cited first, 17 of 17)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations](https://mlsanity.com/paper/1708.00588/) (2017)
- [Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning](https://mlsanity.com/paper/2007.04074/) (2020)
- [From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors](https://mlsanity.com/paper/2203.11070/) (2022)
- [Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methods](https://mlsanity.com/paper/1903.11260/) (2019)
- [A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge](https://mlsanity.com/paper/2209.06257/) (2022)
- [Shape-constrained Symbolic Regression -- Improving Extrapolation with Prior Knowledge](https://mlsanity.com/paper/2103.15624/) (2021)
- [Deep Reinforcement Learning using Genetic Algorithm for Parameter Optimization](https://mlsanity.com/paper/1905.04100/) (2019)
- [Ludwig: a type-based declarative deep learning toolbox](https://mlsanity.com/paper/1909.07930/) (2019)
- [AlphaD3M: Machine Learning Pipeline Synthesis](https://mlsanity.com/paper/2111.02508/) (2021)
- [Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication](https://mlsanity.com/paper/2109.13895/) (2021)
- [Symbolic Regression as Feature Engineering Method for Machine and Deep Learning Regression Tasks](https://mlsanity.com/paper/2311.06028/) (2023)
- [autoBagging: Learning to Rank Bagging Workflows with Metalearning](https://mlsanity.com/paper/1706.09367/) (2017)
- [On Enhancing Genetic Algorithms Using New Crossovers](https://mlsanity.com/paper/1801.02335/) (2018)
- [Mathematical Modeling of BCG-based Bladder Cancer Treatment Using Socio-Demographics](https://mlsanity.com/paper/2307.15084/) (2023)
- [Frouros: A Python library for drift detection in machine learning systems](https://mlsanity.com/paper/2208.06868/) (2022)

## Built on by (most cited first, 1 of 1)

- [Interpretable Transformation and Analysis of Timelines through Learning via Surprisability](https://mlsanity.com/paper/2503.04502/) (2025)

## Abstract (arXiv)

Data-driven models, in general, and machine learning (ML) models, in particular, have gained popularity over recent years with an increased usage of such models across the scientific and engineering domains. When using ML models in realistic and dynamic environments, users need to often handle the challenge of concept drift (CD). In this study, we explore the application of genetic algorithms (GAs) to address the challenges posed by CD in such settings. We propose a novel two-level ensemble ML model, which combines a global ML model with a CD detector, operating as an aggregator for a population of ML pipeline models, each one with an adjusted CD detector by itself responsible for re-training its ML model. In addition, we show one can further improve the proposed model by utilizing off-the-shelf automatic ML methods. Through extensive synthetic dataset analysis, we show that the proposed model outperforms a single ML pipeline with a CD algorithm, particularly in scenarios with unknown CD characteristics. Overall, this study highlights the potential of ensemble ML and CD models obtained through a heuristic and adaptive optimization process such as the GA one to handle complex CD events.