Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment
This work addresses computational bottlenecks for users of evolutionary AutoML, but it is incremental as it builds on existing methods with optimizations.
The paper tackles the problem of resource-intensive computations limiting evolutionary AutoML effectiveness by proposing a modular approach with parallelization, caching, and evaluation stages to improve computational performance in heterogeneous environments, with experiments confirming correctness and effectiveness.
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.