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Computer Science > Machine Learning

arXiv:2301.05102 (cs)
[Submitted on 12 Jan 2023]

Title:Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment

Authors:Nikolay O. Nikitin, Sergey Teryoshkin, Valerii Pokrovskii, Sergey Pakulin, Denis Nasonov
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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 proposed approach. The implemented algorithms are available as a part of the open-source framework FEDOT.
Subjects: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Performance (cs.PF)
Cite as: arXiv:2301.05102 [cs.LG]
  (or arXiv:2301.05102v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2301.05102

Submission history

From: Nikolay Nikitin [view email]
[v1] Thu, 12 Jan 2023 15:59:04 UTC (1,108 KB)
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