Multi-Objective Evolutionary Design of CompositeData-Driven Models

• URL: http://arxiv.org/abs/2103.01301v1
• Date: Mon, 1 Mar 2021 20:45:24 GMT
• Title: Multi-Objective Evolutionary Design of CompositeData-Driven Models
• Authors: Iana S. Polonskaia, Nikolay O. Nikitin, Ilia Revin, Pavel Vychuzhanin, Anna V. Kalyuzhnaya
• Abstract summary: The implemented approach is based on a parameter-free genetic algorithm for model design called GPComp@Free. The experimental results confirm that a multi-objective approach to the model design allows achieving better diversity and quality of obtained models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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