TY - CHAP
T1 - Interactive multiobjective optimization from a learning perspective
AU - Belton, V.
AU - Branke, J.
AU - Eskelinen, P.
AU - Greco, Salvatore
AU - Molina, J.
AU - Ruiz, F.
AU - Slowinski, R.
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-540-88908-3_15
DO - 10.1007/978-3-540-88908-3_15
M3 - Chapter (peer-reviewed)
SN - 9783540889076
VL - 5252
T3 - Lecture notes in computer science
SP - 405
EP - 433
BT - Multiobjective optimization: interactive and evolutionary approaches
A2 - Branke, J.
A2 - Deb, K.
A2 - Miettinen, K.
A2 - Slowinski, R.
PB - Springer
CY - Berlin
ER -