Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment

• URL: http://arxiv.org/abs/2301.05102v1
• Date: Thu, 12 Jan 2023 15:59:04 GMT
• Title: Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment
• Authors: Nikolay O. Nikitin, Sergey Teryoshkin, Valerii Pokrovskii, Sergey Pakulin, Denis Nasonov
• Abstract summary: We propose a modular approach to increase the quality of evolutionary optimization for modelling pipelines with a graph-based structure. The implemented algorithms are available as a part of the open-source framework FEDOT.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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