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Enhancing Surrogate-Based Optimization Through Parallelization

  • Book
  • © 2023

Overview

Authors:
  1. Frederik Rehbach

    1. Fakultät Informatik, TU Dortmund University, Köln, Germany

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  • Presents an in-depth analysis on parallel Surrogate-Based Optimization (SBO) algorithms
  • Introduces a novel benchmarking framework for the fair comparison of parallel SBO algorithms
  • Focuses on the application of parallel SBO

Part of the book series: Studies in Computational Intelligence (SCI, volume 1099)

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Table of contents (5 chapters)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Introduction

    • Frederik Rehbach
    Pages 1-7

  3. Background

    • Frederik Rehbach
    Pages 9-28

  4. Methods/Contributions

    • Frederik Rehbach
    Pages 29-94

  5. Application

    • Frederik Rehbach
    Pages 95-107

  6. Final Evaluation

    • Frederik Rehbach
    Pages 109-114

  7. Back Matter

    Pages 115-115
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Keywords

About this book

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.

Authors and Affiliations

  • Fakultät Informatik, TU Dortmund University, Köln, Germany

    Frederik Rehbach

Bibliographic Information

  • Book Title: Enhancing Surrogate-Based Optimization Through Parallelization

  • Authors: Frederik Rehbach

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-031-30609-9

  • Publisher: Springer Cham

  • eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023

  • Hardcover ISBN: 978-3-031-30608-2Published: 30 May 2023

  • Softcover ISBN: 978-3-031-30611-2Due: 30 June 2023

  • eBook ISBN: 978-3-031-30609-9Published: 29 May 2023

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: X, 115

  • Number of Illustrations: 7 b/w illustrations, 26 illustrations in colour

  • Topics: Computational Intelligence, Data Engineering

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