Interactive multiobjective optimization from a learning perspective

Belton, V. and Branke, J. and Eskelinen, P. and Greco, S. and Molina, J. and Ruiz, F. and Slowinski, R.; Branke, J. and Deb, K. and Mittinen, K. and Slowinski, R., eds. (2008) Interactive multiobjective optimization from a learning perspective. In: Multiobjective Optimization Interactive and Evolutionary Approaches. Lecture Notes in Computer Science, 5252 . Theoretical Computer Science and General, pp. 405-433. ISBN 978-3-540-88907-6 (http://dx.doi.org/10.1007/978-3-540-88908-3_15)

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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.

ORCID iDs

Belton, V. ORCID logoORCID: https://orcid.org/0000-0003-2555-1469, Branke, J., Eskelinen, P., Greco, S., Molina, J., Ruiz, F. and Slowinski, R.; Branke, J., Deb, K., Mittinen, K. and Slowinski, R.
CORE (COnnecting REpositories)