國立高雄大學圖資館 |

登入

回首頁 到查詢結果 [ subject:"Surrogate-based optimization." ]

Enhancing surrogate-based optimization through parallelization

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Enhancing surrogate-based optimization through parallelizationby Frederik Rehbach.
作者:	Rehbach, Frederik.
出版者:	Cham :Springer Nature Switzerland :2023.
面頁冊數:	1 online resource (x, 115 p.) :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Surrogate-based optimization.
電子資源:	https://doi.org/10.1007/978-3-031-30609-9
ISBN:	9783031306099$q(electronic bk.)

Enhancing surrogate-based optimization through parallelization
Rehbach, Frederik.

Enhancing surrogate-based optimization through parallelization[electronic resource] /by Frederik Rehbach. - Cham :Springer Nature Switzerland :2023. - 1 online resource (x, 115 p.) :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v. 10991860-9503 ;. - Studies in computational intelligence ;v. 216..

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.

ISBN: 9783031306099$q(electronic bk.)

Standard No.: 10.1007/978-3-031-30609-9doiSubjects--Topical Terms:

944539
Surrogate-based optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

Enhancing surrogate-based optimization through parallelization
LDR:02453nmm a2200337 a 4500 001 637955
003 DE-He213
005 20230529151810.0
006 m d
007 cr nn 008maaau
008 230921s2023 sz s 0 eng d
020 $a 9783031306099$q(electronic bk.)
020 $a 9783031306082$q(paper)
024 7 $a 10.1007/978-3-031-30609-9 $2 doi
035 $a 978-3-031-30609-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QA402.5
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 519.6 $2 23
090 $a QA402.5 $b .R345 2023
100 1 $a Rehbach, Frederik. $3 944538
245 1 0 $a Enhancing surrogate-based optimization through parallelization $h [electronic resource] / $c by Frederik Rehbach.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2023.
300 $a 1 online resource (x, 115 p.) : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-9503 ; $v v. 1099
505 0 $a Introduction -- Background -- Methods/Contributions -- Application -- Final Evaluation.
520 $a 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.
650 0 $a Surrogate-based optimization. $3 944539
650 0 $a Parallel algorithms. $3 204612
650 1 4 $a Computational Intelligence. $3 338479
650 2 4 $a Data Engineering. $3 839346
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Studies in computational intelligence ; $v v. 216. $3 380871
856 4 0 $u https://doi.org/10.1007/978-3-031-30609-9
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)