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A Gentle Introduction to Sequential Parameter Optimization

  • There is a strong need for sound statistical analysis of simulation and optimization algorithms. Based on this analysis, improved parameter settings can be determined. This will be referred to as tuning. Model-based investigations are common approaches in simulation and optimization. The sequential parameter optimization toolbox (SPOT), which is implemented as a package for the statistical programming language R, provides sophisticated means for tuning and understanding simulation and optimization algorithms. The toolbox includes methods for tuning based on classical regression and analysis of variance techniques; tree-based models such as classification and regressions trees (CART) and random forest; Gaussian process models (Kriging), and combinations of different meta-modeling approaches. This article exemplifies how an existing optimization algorithm, namely simulated annealing, can be tuned using the SPOT framework.

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Metadaten
Author:Thomas Bartz-BeielsteinGND, Martin Zaefferer
URN:urn:nbn:de:hbz:832-cos-191
Series (Serial Number):CIplus (1/2012)
Document Type:Report
Language:German
Year of Completion:2012
Release Date:2012/09/04
Tag:Computational Intelligence; Parametertuning; Sequentielle Parameter Optimierung
GND Keyword:Globale Optimierung; Modellierung; Optimierung; Simulated annealing; Simulation; Soft Computing; Versuchsplanung
Contributor:Thomas Bartz-Beielstein
Institutes and Central Facilities:Fakultät für Informatik und Ingenieurwissenschaften (F10) / Fakultät 10 / Institut für Informatik
Dewey Decimal Classification:000 Allgemeines, Informatik, Informationswissenschaft / 000 Allgemeines, Wissenschaft / 004 Informatik
Open Access:Open Access
Licence (German):License LogoCreative Commons - Namensnennung, Nicht kommerziell, Keine Bearbeitung