  Resource-intensive computations are a major factor that limits the
effectiveness of automated machine learning solutions. In the paper, we propose
a modular approach that can be used to increase the quality of evolutionary
optimization for modelling pipelines with a graph-based structure. It consists
of several stages - parallelization, caching and evaluation. Heterogeneous and
remote resources can be involved in the evaluation stage. The conducted
experiments confirm the correctness and effectiveness of the proposed approach.
The implemented algorithms are available as a part of the open-source framework
FEDOT.
