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


    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
    Number of pages29
    ISBN (Print)9783540889076
    Publication statusPublished - 2008

    Publication series

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


    Dive into the research topics of 'Interactive multiobjective optimization from a learning perspective'. Together they form a unique fingerprint.

    Cite this