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.
Author: | Thomas Bartz-BeielsteinGND, Martin Zaefferer |
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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: | Optimierung; Globale Optimierung; Simulation; Simulated annealing; Versuchsplanung; Modellierung; Soft Computing |
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 / 004 Informatik |
Open Access: | Open Access |
Licence (German): | ![]() |