2023
DOI: 10.48550/arxiv.2301.05102
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Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment

Abstract: 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 pr… Show more

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