mlsanityData to 7 Oct 2026

Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelines

arXiv 2106.15397 ↗First posted 26 Jun 2021cs.LGcited by 13

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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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 19 of its 70 references that are in the map. First posted on arXiv 26 Jun 2021.

Builds on

19 of 70 references.

References · 19

  1. 2018MnasNet: Platform-Aware Neural Architecture Search for Mobilecited by 937
  2. 2018Regularized Evolution for Image Classifier Architecture Searchcited by 902
  3. 2019AutoML: A Survey of the State-of-the-Artcited by 388
  4. 2020AutoGluon-Tabular: Robust and Accurate AutoML for Structured Datacited by 368
  5. 2020Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)cited by 318
  6. 2018Auto-Keras: An Efficient Neural Architecture Search Systemcited by 169
  7. 2020AutoML-Zero: Evolving Machine Learning Algorithms From Scratchcited by 137
  8. 2018Tunability: Importance of Hyperparameters of Machine Learning Algorithmscited by 98
  9. 2017Limits of End-to-End Learningcited by 35
  10. 2019SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detectioncited by 25
  11. 2021Whither AutoML? Understanding the Role of Automation in Machine Learning Workflowscited by 22
  12. 2019DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filteringcited by 21
7 more
  1. 2021Incremental Search Space Construction for Machine Learning Pipeline Synthesiscited by 6
  2. 2018Building a Reproducible Machine Learning Pipelinecited by 4
  3. 2020An Adaptive and Near Parameter-free Evolutionary Computation Approach Towards True Automation in AutoMLcited by 4
  4. 2021Multi-Objective Evolutionary Design of Composite Data-Driven Modelscited by 4
  5. 2020A Generalized Stacking for Implementing Ensembles of Gradient Boosting Machinescited by 3
  6. 2019REBEC: Robust Evolutionary-based Calibration Approach for the Numerical Wind Wave Modelcited by 2
  7. 2020The data-driven physical-based equations discovery using evolutionary approachcited by 2

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Citing papers per year: 2022: 1; 2023: 4; 2024: 4; 2025: 4.

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  1. 2024Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Surveycited by 19
  2. 2024Automated data processing and feature engineering for deep learning and big data applications: a surveycited by 14
  3. 2023Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Visioncited by 11
  4. 2025SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Sciencecited by 7
  5. 2025CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learningcited by 2
  6. 2023Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environmentcited by 1
  7. 2023Integration Of Evolutionary Automated Machine Learning With Structural Sensitivity Analysis For Composite Pipelinescited by 1
  8. 2024Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methodscited by 1
  9. 2025LightAutoDS-Tab: Multi-AutoML Agentic System for Tabular Datacited by 1
  10. 2025AutoIntent: AutoML for Text Classificationcited by 1
  11. 2022On the balance between the training time and interpretability of neural ODE for time series modellingcited by 0
  12. 2023A knowledge-driven AutoML architecturecited by 0
1 more
  1. 2024Forecasting Imports in OECD Member Countries and Iran by Using Neural Network Algorithms of LSTMcited by 0