Enhancing Surrogate-Based Optimization Through Parallelization / by Frederik Rehbach.

Format:

Book

Author/Creator:

Rehbach, Frederik.

Series:

Studies in Computational Intelligence, 1860-9503 ; 1099

Language:

English

Subjects (All):

Computational intelligence.

Engineering—Data processing.

Computational Intelligence.

Data Engineering.

Local Subjects:

Computational Intelligence.

Data Engineering.

Physical Description:

1 online resource (123 pages)

Edition:

1st ed. 2023.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2023.

Summary:

This book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (SBO) and parallelization, to efficiently search for optimal parameter setups with as few function evaluations as possible. Through in-depth analysis, the need for parallel SBO solvers is emphasized, and it is demonstrated that they outperform model-free algorithms in scenarios with a low evaluation budget. The SBO approach helps practitioners save significant amounts of time and resources in hyperparameter tuning as well as other optimization projects. As a highlight, a novel framework for objectively comparing the efficiency of parallel SBO algorithms is introduced, enabling practitioners to evaluate and select the most effective approach for their specific use case. Based on practical examples, decision support is delivered, detailing which parts of industrial optimization projects can be parallelized and how to prioritize which parts to parallelize first. By following the framework, practitioners can make informed decisions about how to allocate resources and optimize their models efficiently.

Contents:

Introduction

Background

Methods/Contributions

Application

Final Evaluation.

Notes:

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Rehbach, Frederik Enhancing Surrogate-Based Optimization Through Parallelization

ISBN:

9783031306099

OCLC:

1381457437