Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment

Autor:	Nikitin, Nikolay O., Teryoshkin, Sergey, Pokrovskii, Valerii, Pakulin, Sergey, Nasonov, Denis
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Neural and Evolutionary Computing Computer Science - Performance
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2301.05102 Zobrazit plný text záznamu View this record from Arxiv