# Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelines

arXiv: https://arxiv.org/abs/2106.15397 · HTML: https://mlsanity.com/paper/2106.15397/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 70 references that are in the map.
- First posted on arXiv 26 Jun 2021.

## Builds on (most cited first, 19 of 19)

- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)](https://mlsanity.com/paper/2003.12206/) (2020)
- [Auto-Keras: An Efficient Neural Architecture Search System](https://mlsanity.com/paper/1806.10282/) (2018)
- [AutoML-Zero: Evolving Machine Learning Algorithms From Scratch](https://mlsanity.com/paper/2003.03384/) (2020)
- [Tunability: Importance of Hyperparameters of Machine Learning Algorithms](https://mlsanity.com/paper/1802.09596/) (2018)
- [Limits of End-to-End Learning](https://mlsanity.com/paper/1704.08305/) (2017)
- [SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detection](https://mlsanity.com/paper/1911.09929/) (2019)
- [Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows](https://mlsanity.com/paper/2101.04834/) (2021)
- [DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering](https://mlsanity.com/paper/1911.00061/) (2019)
- [Incremental Search Space Construction for Machine Learning Pipeline Synthesis](https://mlsanity.com/paper/2101.10951/) (2021)
- [Building a Reproducible Machine Learning Pipeline](https://mlsanity.com/paper/1810.04570/) (2018)
- [An Adaptive and Near Parameter-free Evolutionary Computation Approach Towards True Automation in AutoML](https://mlsanity.com/paper/2001.10178/) (2020)
- [Multi-Objective Evolutionary Design of Composite Data-Driven Models](https://mlsanity.com/paper/2103.01301/) (2021)
- [A Generalized Stacking for Implementing Ensembles of Gradient Boosting Machines](https://mlsanity.com/paper/2010.06026/) (2020)
- [REBEC: Robust Evolutionary-based Calibration Approach for the Numerical Wind Wave Model](https://mlsanity.com/paper/1906.08587/) (2019)
- [The data-driven physical-based equations discovery using evolutionary approach](https://mlsanity.com/paper/2004.01680/) (2020)

## Built on by (most cited first, 13 of 13)

- [Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey](https://mlsanity.com/paper/2411.10478/) (2024)
- [Automated data processing and feature engineering for deep learning and big data applications: a survey](https://mlsanity.com/paper/2403.11395/) (2024)
- [Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Vision](https://mlsanity.com/paper/2303.07476/) (2023)
- [SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Science](https://mlsanity.com/paper/2503.23314/) (2025)
- [CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learning](https://mlsanity.com/paper/2502.03946/) (2025)
- [Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment](https://mlsanity.com/paper/2301.05102/) (2023)
- [Integration Of Evolutionary Automated Machine Learning With Structural Sensitivity Analysis For Composite Pipelines](https://mlsanity.com/paper/2312.14770/) (2023)
- [Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods](https://mlsanity.com/paper/2403.08352/) (2024)
- [LightAutoDS-Tab: Multi-AutoML Agentic System for Tabular Data](https://mlsanity.com/paper/2507.13413/) (2025)
- [AutoIntent: AutoML for Text Classification](https://mlsanity.com/paper/2509.21138/) (2025)
- [On the balance between the training time and interpretability of neural ODE for time series modelling](https://mlsanity.com/paper/2206.03304/) (2022)
- [A knowledge-driven AutoML architecture](https://mlsanity.com/paper/2311.17124/) (2023)
- [Forecasting Imports in OECD Member Countries and Iran by Using Neural Network Algorithms of LSTM](https://mlsanity.com/paper/2402.01648/) (2024)

## Abstract (arXiv)

The effectiveness of the machine learning methods for real-world tasks depends on the proper structure of the modeling pipeline. The proposed approach is aimed to automate the design of composite machine learning pipelines, which is equivalent to computation workflows that consist of models and data operations. The approach combines key ideas of both automated machine learning and workflow management systems. It designs the pipelines with a customizable graph-based structure, analyzes the obtained results, and reproduces them. The evolutionary approach is used for the flexible identification of pipeline structure. The additional algorithms for sensitivity analysis, atomization, and hyperparameter tuning are implemented to improve the effectiveness of the approach. Also, the software implementation on this approach is presented as an open-source framework. The set of experiments is conducted for the different datasets and tasks (classification, regression, time series forecasting). The obtained results confirm the correctness and effectiveness of the proposed approach in the comparison with the state-of-the-art competitors and baseline solutions.