Enhancing Surrogate-Based Optimization Through Parallelization
Rehbach, Frederik.
Enhancing Surrogate-Based Optimization Through Parallelization [electronic resource] / by Frederik Rehbach. - 1st ed. 2023. - X, 115 p. 33 illus., 26 illus. in color. online resource. - Studies in Computational Intelligence, 1099 1860-9503 ; . - Studies in Computational Intelligence, 1099 .
Acceso multiusuario
Introduction -- Background -- Methods/Contributions -- Application -- Final Evaluation.
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.
9783031306099
Computational intelligence.
Engineering--Data processing.
Computational Intelligence.
Data Engineering.
Q342
006.3
Enhancing Surrogate-Based Optimization Through Parallelization [electronic resource] / by Frederik Rehbach. - 1st ed. 2023. - X, 115 p. 33 illus., 26 illus. in color. online resource. - Studies in Computational Intelligence, 1099 1860-9503 ; . - Studies in Computational Intelligence, 1099 .
Acceso multiusuario
Introduction -- Background -- Methods/Contributions -- Application -- Final Evaluation.
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.
9783031306099
Computational intelligence.
Engineering--Data processing.
Computational Intelligence.
Data Engineering.
Q342
006.3
