Improvement of Computational Performance of Evolutionary AutoML in a Heterogeneous Environment

TL;DR

This paper presents a modular approach to enhance the computational efficiency of evolutionary AutoML in heterogeneous environments by leveraging parallelization, caching, and remote resource evaluation, validated through experiments.

Contribution

It introduces a novel modular framework that improves evolutionary AutoML performance by utilizing parallelization, caching, and remote resource evaluation, integrated into FEDOT.

Findings

Confirmed the correctness of the approach

Demonstrated increased efficiency in AutoML processes

Validated with experiments in heterogeneous environments

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.