Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment
Machine Learning
2023-01-13 v1 Neural and Evolutionary Computing
Performance
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.
Keywords
Cite
@article{arxiv.2301.05102,
title = {Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment},
author = {Nikolay O. Nikitin and Sergey Teryoshkin and Valerii Pokrovskii and Sergey Pakulin and Denis Nasonov},
journal= {arXiv preprint arXiv:2301.05102},
year = {2023}
}