Modeling Behavior-Realistic Artificial Decision-Makers to Test Preference-Based Multiple Objective Optimization Methods:Report of Working Group #1 | Zendy

Branke, Juergen | Zendy; Corrente, Salvatore | Zendy; Greco, Salvatore | Zendy; Kadzinski, Miłosz | Zendy; Lopez-Ibanez, Manuel | Zendy; Mousseau, Vincent | Zendy; Munerato, Mauro | Zendy; Slowinski, Roman | Zendy

Open Access

Modeling Behavior-Realistic Artificial Decision-Makers to Test Preference-Based Multiple Objective Optimization Methods:Report of Working Group #1

Author(s) -

Branke, Juergen,

Corrente, Salvatore,

Greco, Salvatore,

Kadzinski, Miłosz,

Lopez-Ibanez, Manuel,

Mousseau, Vincent,

Munerato, Mauro,

Slowinski, Roman

Publication year - 2015

Publication title -

dagstuhl reports

Language(s) - English

DOI - 10.4230/dagrep.5.1.96

This report documents the program and outcomes of the Dagstuhl Seminar 15031 Understanding Complexity in Multiobjective Optimization. This seminar carried on the series of four previous Dagstuhl Seminars (04461, 06501, 09041 and 12041) that were focused on Multiobjective Optimization, and strengthening the links between the Evolutionary Multiobjective Optimization (EMO) and Multiple Criteria Decision Making (MCDM) communities. The purpose of the seminar was to bring together researchers from the two communities to take part in a wide-ranging discussion about the different sources and impacts of complexity in multiobjective optimization. The outcome was a clarified viewpoint of complexity in the various facets of multiobjective optimization, leading to several research initiatives with innovative approaches for coping with complexity.

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