Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment
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Abstract (arXiv)
Resource-intensive computations are a major factor that limits the effectiveness of automated machine learning solutions. In the paper, we propose a modular approach that can be used to increase the quality of evolutionary optimization for modelling pipelines with a graph-based structure. It consists of several stages - parallelization, caching and evaluation. Heterogeneous and remote resources can be involved in the evaluation stage. The conducted experiments confirm the correctness and effectiveness of the proposed approach. The implemented algorithms are available as a part of the open-source framework FEDOT.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 44 references that are in the map. First posted on arXiv 12 Jan 2023.
Builds on
10 of 44 references.
References · 10
- 2017Ray: A Distributed Framework for Emerging AI Applicationscited by 527
- 2019AutoML: A Survey of the State-of-the-Artcited by 390
- 2020AutoGluon-Tabular: Robust and Accurate AutoML for Structured Datacited by 383
- 2020AutoML to Date and Beyond: Challenges and Opportunitiescited by 63
- 2021LightAutoML: AutoML Solution for a Large Financial Services Ecosystemcited by 32
- 2021Towards Green Automated Machine Learning: Status Quo and Future Directionscited by 25
- 2021Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelinescited by 13
- 2018Layered TPOT: Speeding up Tree-based Pipeline Optimizationcited by 7
- 2021Multi-Objective Evolutionary Design of Composite Data-Driven Modelscited by 4
- 2022An Empirical Study on the Usage of Automated Machine Learning Toolscited by 4
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Citing papers per year: 2025: 1.
1 paper.