  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.
