Change search
ReferencesLink to record
Permanent link

Direct link
On the Performance of Classification Algorithms for Learning Pareto-Dominance Relations
University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science. (Simulation-based optimization)
University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science. (Simulation-based optimization)ORCID iD: 0000-0003-0111-1776
Michigan State University. (BEACON Center for the Study of Evolution in Action)
2014 (English)In: Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC), IEEE Press, 2014, 1139-1146 p.Conference paper (Refereed)
Abstract [en]

Multi-objective evolutionary algorithms (MOEAs)are often criticized for their high-computational costs. Thisbecomes especially relevant in simulation-based optimizationwhere the objectives lack a closed form and are expensive toevaluate. Over the years, meta-modeling or surrogate modelingtechniques have been used to build inexpensive approximationsof the objective functions which reduce the overall number offunction evaluations (simulations). Some recent studies however,have pointed out that accurate models of the objective functionsmay not be required at all since evolutionary algorithms onlyrely on the relative ranking of candidate solutions. Extendingthis notion to MOEAs, algorithms which can ‘learn’ Paretodominancerelations can be used to compare candidate solutionsunder multiple objectives. With this goal in mind, in thispaper, we study the performance of ten different off-the-shelfclassification algorithms for learning Pareto-dominance relationsin the ZDT test suite of benchmark problems. We considerprediction accuracy and training time as performance measureswith respect to dimensionality and skewness of the training data.Being a preliminary study, this paper does not include results ofintegrating the classifiers into the search process of MOEAs.

Place, publisher, year, edition, pages
IEEE Press, 2014. 1139-1146 p.
Keyword [en]
Meta-modeling, Multi-objective optimization, Classification algorithms, Pareto-dominance, Machine learning
National Category
Computer Science
Research subject
URN: urn:nbn:se:his:diva-9680ISBN: 978-1-4799-1488-3OAI: diva2:734365
2014 IEEE World Congress on Computational Intelligence
Knowledge Foundation, 41128
Available from: 2014-07-16 Created: 2014-07-16 Last updated: 2015-12-18Bibliographically approved

Open Access in DiVA

bandaru2014performance(153 kB)468 downloads
File information
File name FULLTEXT01.pdfFile size 153 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Bandaru, SunithNg, Amos
By organisation
The Virtual Systems Research CentreSchool of Engineering Science
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 468 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 1056 hits
ReferencesLink to record
Permanent link

Direct link