Multiobjective Evolutionary Design Of Composite Datadriven Models
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**arXiv ID:** 2103.01301 **Authors:** Iana S. Polonskaia, Nikolay O. Nikitin, Ilia Revin, Pavel Vychuzhanin, Anna V. Kalyuzhnaya **Published:** 2021-03-01T20:45:24Z **Abstract:** 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...
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# Multi-Objective Evolutionary Design of Composite Data-Driven Models
**arXiv ID:** 2103.01301
**Authors:** Iana S. Polonskaia, Nikolay O. Nikitin, Ilia Revin, Pavel Vychuzhanin, Anna V. Kalyuzhnaya
**Published:** 2021-03-01T20:45:24Z
**Abstract:**
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.
## Skill Description
This skill is generated from the arXiv paper: Multi-Objective Evolutionary Design of Composite Data-Driven Models (2103.01301).
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## References
- [arXiv:2103.01301](http://arxiv.org/abs/2103.01301v2)
