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020 _a9783031306099
_9978-3-031-30609-9
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072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
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_2thema
082 0 4 _a006.3
_223
100 1 _aRehbach, Frederik.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aEnhancing Surrogate-Based Optimization Through Parallelization
_h[electronic resource] /
_cby Frederik Rehbach.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2023.
300 _aX, 115 p. 33 illus., 26 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v1099
500 _aAcceso multiusuario
505 0 _aIntroduction -- Background -- Methods/Contributions -- Application -- Final Evaluation.
520 _aThis 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.
541 _fUABC ;
_cPerpetuidad
650 0 _aComputational intelligence.
650 0 _aEngineering
_xData processing.
650 1 4 _aComputational Intelligence.
650 2 4 _aData Engineering.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031306082
776 0 8 _iPrinted edition:
_z9783031306105
776 0 8 _iPrinted edition:
_z9783031306112
830 0 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v1099
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-30609-9
912 _aZDB-2-INR
912 _aZDB-2-SXIT
942 _cLIBRO_ELEC
999 _c261657
_d261656