# Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment

arXiv: https://arxiv.org/abs/2301.05102 · HTML: https://mlsanity.com/paper/2301.05102/

## Key facts

- Cited by 1 papers 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 (most cited first, 10 of 10)

- [Ray: A Distributed Framework for Emerging AI Applications](https://mlsanity.com/paper/1712.05889/) (2017)
- [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)
- [AutoML to Date and Beyond: Challenges and Opportunities](https://mlsanity.com/paper/2010.10777/) (2020)
- [LightAutoML: AutoML Solution for a Large Financial Services Ecosystem](https://mlsanity.com/paper/2109.01528/) (2021)
- [Towards Green Automated Machine Learning: Status Quo and Future Directions](https://mlsanity.com/paper/2111.05850/) (2021)
- [Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelines](https://mlsanity.com/paper/2106.15397/) (2021)
- [Layered TPOT: Speeding up Tree-based Pipeline Optimization](https://mlsanity.com/paper/1801.06007/) (2018)
- [Multi-Objective Evolutionary Design of Composite Data-Driven Models](https://mlsanity.com/paper/2103.01301/) (2021)
- [An Empirical Study on the Usage of Automated Machine Learning Tools](https://mlsanity.com/paper/2208.13116/) (2022)

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

- [Automated Evolutionary Optimization for Resource-Efficient Neural Network Training](https://mlsanity.com/paper/2510.09566/) (2025)

## 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.