2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings, pages 926-933

Multi-Objective Evolutionary Design of Composite Data-Driven Models

Publication typeProceedings Article
Publication date2021-06-28
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 us to achieve better diversity and quality of obtained models. The implemented approach is available as a part of the open-source AutoML framework FEDOT.

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Polonskaia I. S. et al. Multi-Objective Evolutionary Design of Composite Data-Driven Models // 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings. 2021. pp. 926-933.
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Polonskaia I. S., Nikitin N. O., Revin I., Vychuzhanin P., Kalyuzhnaya A. Multi-Objective Evolutionary Design of Composite Data-Driven Models // 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings. 2021. pp. 926-933.
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TY - CPAPER
DO - 10.1109/CEC45853.2021.9504773
UR - https://doi.org/10.1109%2FCEC45853.2021.9504773
TI - Multi-Objective Evolutionary Design of Composite Data-Driven Models
T2 - 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings
AU - Polonskaia, Iana S
AU - Nikitin, Nikolay O
AU - Revin, Ilia
AU - Vychuzhanin, Pavel
AU - Kalyuzhnaya, Anna
PY - 2021
DA - 2021/06/28 00:00:00
SP - 926-933
ER -
BibTex
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BibTex Copy
@inproceedings{2021_Polonskaia
author = {Iana S Polonskaia and Nikolay O Nikitin and Ilia Revin and Pavel Vychuzhanin and Anna Kalyuzhnaya},
title = {Multi-Objective Evolutionary Design of Composite Data-Driven Models},
year = {2021},
pages = {926--933},
month = {jun}
}
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