Multi-Objective Evolutionary Design of Composite Data-Driven Models
This work addresses the challenge of automating machine learning pipeline design for users seeking efficient modeling solutions, though it appears incremental as it builds on existing evolutionary and AutoML methods.
The paper tackles the problem of automating the design of composite data-driven models, such as heterogeneous pipelines with machine learning and preprocessing blocks, using a multi-objective evolutionary approach. The result is that this method achieves better diversity and quality of models, as confirmed by experiments, and is implemented in the open-source AutoML framework FEDOT.
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.