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Beyond Particular Problem Instances: How to Create Meaningful and Generalizable Results

  • Computational intelligence methods have gained importance in several real-world domains such as process optimization, system identification, data mining, or statistical quality control. Tools are missing, which determine the applicability of computational intelligence methods in these application domains in an objective manner. Statistics provide methods for comparing algorithms on certain data sets. In the past, several test suites were presented and considered as state of the art. However, there are several drawbacks of these test suites, namely: (i) problem instances are somehow artificial and have no direct link to real-world settings; (ii) since there is a fixed number of test instances, algorithms can be fitted or tuned to this specific and very limited set of test functions; (iii) statistical tools for comparisons of several algorithms on several test problem instances are relatively complex and not easily to analyze. We propose a methodology to overcome these difficulties. It is based on standard ideas from statistics: analysis of variance and its extension to mixed models. This paper combines essential ideas from two approaches: problem generation and statistical analysis of computer experiments.

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Author:Thomas Bartz-BeielsteinGND
Series (Serial Number):CIplus (3/2012)
Document Type:Report
Year of Completion:2012
Release Date:2012/11/22
Tag:Mixed Models; Optimization; Statistics
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):License LogoCreative Commons - Namensnennung, Nicht kommerziell, Keine Bearbeitung