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Post-analysis of multi-objective optimization solutions using decision trees
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och Automatiseringsteknik, Production and Automation Engineering)
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. University of Skövde. (Produktion och Automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-0111-1776
Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden.
2015 (English)In: Intelligent Data Analysis, ISSN 1088-467X, E-ISSN 1571-4128, Vol. 19, no 2, 259-278 p.Article in journal (Refereed) Published
Abstract [en]

Evolutionary algorithms are often applied to solve multi-objective optimization problems. Such algorithms effectively generate solutions of wide spread, and have good convergence properties. However, they do not provide any characteristics of the found optimal solutions, something which may be very valuable to decision makers. By performing a post-analysis of the solution set from multi-objective optimization, relationships between the input space and the objective space can be identified. In this study, decision trees are used for this purpose. It is demonstrated that they may effectively capture important characteristics of the solution sets produced by multi-objective optimization methods. It is furthermore shown that the discovered relationships may be used for improving the search for additional solutions. Two multi-objective problems are considered in this paper; a well-studied benchmark function problem with on a beforehand known optimal Pareto front, which is used for verification purposes, and a multi-objective optimization problem of a real-world production system. The results show that useful relationships may be identified by employing decision tree analysis of the solution sets from multi-objective optimizations.

Place, publisher, year, edition, pages
IOS Press, 2015. Vol. 19, no 2, 259-278 p.
Keyword [en]
Data Mining, Multi-Objective Optimization, Decision Trees
National Category
Computer Science
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-10974DOI: 10.3233/IDA-150716ISI: 000353062400004Scopus ID: 2-s2.0-84928542539OAI: oai:DiVA.org:his-10974DiVA: diva2:815022
Funder
VINNOVA
Available from: 2015-05-29 Created: 2015-05-29 Last updated: 2016-01-12Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf