mlsanityData to 7 Oct 2026

Multi-Objective Evolutionary Design of Composite Data-Driven Models

arXiv 2103.01301 ↗First posted 1 Mar 2021cs.NE cs.AI cs.LGcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 9 of its 28 references that are in the map. First posted on arXiv 1 Mar 2021.

Builds on

9 of 28 references.

References · 9

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  9. 2020The data-driven physical-based equations discovery using evolutionary approachcited by 2

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Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 0; 2025: 1.

4 papers.

  1. 2022Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overviewcited by 37
  2. 2021Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelinescited by 13
  3. 2023Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environmentcited by 1
  4. 2025Automated Evolutionary Optimization for Resource-Efficient Neural Network Trainingcited by 0