Titlebook: Enhancing Surrogate-Based Optimization Through Parallelization; Frederik Rehbach Book 2023 The Editor(s) (if applicable) and The Author(s)

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Enhancing Surrogate-Based Optimization Through Parallelization

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Introduction,cost of computer experiments such as Computational Fluid Dynamics (CFD) simulations. In such a scenario, often only a few tens or hundreds of expensive functions evaluations are feasible due to a strict budget of time or money that has to be met. These hard limits require the use of very sample-efficient optimizers.

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Book 2023chine 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 emphasi

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1860-949X ir comparison of parallel SBO algorithms.Focuses on the applThis 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-Base

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https://doi.org/10.1057/9781403914279literature where needed. The chapter continues by presenting a taxonomy for parallel SBO (Sect.?.) and giving recommendations for practitioners in the field of SBO. The chapter is concluded with a literature review covering existing SBO methods (Sect.?.).

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https://doi.org/10.1007/978-1-349-12218-9to-evaluate functions. Rigorous methods for analyzing and assessing algorithm performance are required before any improvements can be made to optimization algorithms. Benchmarks and well-chosen test functions are essential to gain an unbiased insight into optimization algorithms.