# Multi-Objective Evolutionary Design of Composite Data-Driven Models

arXiv: https://arxiv.org/abs/2103.01301 · HTML: https://mlsanity.com/paper/2103.01301/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 1 Mar 2021.

## Builds on (most cited first, 9 of 9)

- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison](https://mlsanity.com/paper/1703.00512/) (2017)
- [DeepArchitect: Automatically Designing and Training Deep Architectures](https://mlsanity.com/paper/1704.08792/) (2017)
- [DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering](https://mlsanity.com/paper/1911.00061/) (2019)
- [Multi-Objective Automatic Machine Learning with AutoxgboostMC](https://mlsanity.com/paper/1908.10796/) (2019)
- [An Adaptive and Near Parameter-free Evolutionary Computation Approach Towards True Automation in AutoML](https://mlsanity.com/paper/2001.10178/) (2020)
- [The data-driven physical-based equations discovery using evolutionary approach](https://mlsanity.com/paper/2004.01680/) (2020)

## Built on by (most cited first, 4 of 4)

- [Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overview](https://mlsanity.com/paper/2206.07438/) (2022)
- [Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelines](https://mlsanity.com/paper/2106.15397/) (2021)
- [Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment](https://mlsanity.com/paper/2301.05102/) (2023)
- [Automated Evolutionary Optimization for Resource-Efficient Neural Network Training](https://mlsanity.com/paper/2510.09566/) (2025)

## Abstract (arXiv)

In this paper, a multi-objective approach for the design of composite data-driven mathematical models is proposed. It allows automating the identification of graph-based heterogeneous pipelines that consist of different blocks: machine learning models, data preprocessing blocks, etc. The implemented approach is based on a parameter-free genetic algorithm (GA) for model design called GPComp@Free. It is developed to be part of automated machine learning solutions and to increase the efficiency of the modeling pipeline automation. A set of experiments was conducted to verify the correctness and efficiency of the proposed approach and substantiate the selected solutions. The experimental results confirm that a multi-objective approach to the model design allows achieving better diversity and quality of obtained models. The implemented approach is available as a part of the open-source AutoML framework FEDOT.