Enhancing Surrogate-Based Optimization Through Parallelization

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.

Enhancing Surrogate-Based Optimization Through Parallelization features in the following genres: Artificial intelligence, Databases

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Enhancing Surrogate-Based Optimization Through Parallelization was written by Frederik Rehbach and published by Springer an imprint of Springer Nature Switzerland

Enhancing Surrogate-Based Optimization Through Parallelization has 115 pages

Yes it is part of Studies in Computational Intelligence series

£134.99, reduced from £149.99. Not Available.