Multi-Objective Evolutionary Design of Composite Data-Driven Models

TL;DR

This paper introduces GPComp@Free, a parameter-free genetic algorithm for automating the design of complex, heterogeneous data-driven models within AutoML, improving diversity and quality of models.

Contribution

It presents a novel multi-objective genetic algorithm for automated design of composite data-driven models, integrated into an open-source AutoML framework.

Findings

Enhanced diversity of models achieved

Improved model quality demonstrated

Efficient automation of pipeline design confirmed

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.