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In a Nutshell: Sequential Parameter Optimization

  • The performance of optimization algorithms relies crucially on their parameterizations. Finding good parameter settings is called algorithm tuning. Using a simple simulated annealing algorithm, we will demonstrate how optimization algorithms can be tuned using the Sequential Parameter Optimization Toolbox (SPOT). SPOT provides several tools for automated and interactive tuning. The underlying concepts of the SPOT approach are explained. This includes key techniques such as exploratory fitness landscape analysis and response surface methodology. Many examples illustrate how SPOT can be used for understanding the performance of algorithms and gaining insight into algorithm behavior. Furthermore, we demonstrate how SPOT can be used as an optimizer and how a sophisticated ensemble approach is able to combine several meta models via stacking.

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Metadaten
Author:Thomas Bartz-BeielsteinGND, Lorenzo Gentile, Martin Zaefferer
URN:urn:nbn:de:hbz:832-cos4-5928
Series (Serial Number):CIplus (7/2017)
Document Type:Report
Language:English
Year of Completion:2017
Release Date:2017/12/19
Tag:Algorithm Tuning; Optimization; R; SPOT; Surrogate Models
Pagenumber:46
Institutes and Central Facilities:Fakultät für Informatik und Ingenieurwissenschaften (F10)
Open Access:Open Access
Licence (German):License LogoCreative Commons - Namensnennung, Nicht kommerziell, Keine Bearbeitung