Interactive multiobjective optimization from a learning perspective

V. Belton, J. Branke, P. Eskelinen, Salvatore Greco, J. Molina, F. Ruiz, R. Slowinski

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Abstract

Learning is inherently connected with Interactive Multiobjective Optimization (IMO), therefore, a systematic analysis of IMO from the learning perspective is worthwhile. After an introduction to the nature and the interest of learning within IMO, we consider two complementary aspects of learning: individual learning, i.e., what the decision maker can learn, and model or machine learning, i.e., what the formal model can learn in the course of an IMO procedure. Finally, we discuss how one might investigate learning experimentally, in order to understand how to better support decision makers. Experiments involving a human decision maker or a virtual decision maker are considered.
Original languageEnglish
Title of host publicationMultiobjective optimization: interactive and evolutionary approaches
EditorsJ. Branke, K. Deb, K. Miettinen, R. Slowinski
Place of PublicationBerlin
PublisherSpringer
Pages405-433
Number of pages29
Volume5252
Edition5252
ISBN (Print)9783540889076
DOIs
Publication statusPublished - 2008

Publication series

NameLecture notes in computer science
PublisherSpringer
Number5252
ISSN (Print)0302-9743

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