TY - CHAP
T1 - Interactive evolutionary multiobjective optimization using robust ordinal regression
AU - Branke, J.
AU - Greco, Salvatore
AU - Slowinski, R.
AU - Zielniewicz, P.
PY - 2009/4
Y1 - 2009/4
N2 - This paper proposes the Necessary-preference-enhanced Evolutionary Multiobjective Optimizer (NEMO), a combination of an evolutionary multiobjective optimization method, NSGA-II, and an interactive multiobjective optimization method, GRIP. In the course of NEMO, the decision maker is able to introduce preference information in a holistic way, by simply comparing some pairs of solutions and specifying which solution is preferred, or comparing intensities of preferences between pairs of solutions. From this information, the set of all compatible value functions is derived using GRIP, and a properly modified version of NSGA-II is then used to search for a representative set of all Pareto-optimal solutions compatible with this set of derived value functions. As we show, this allows to focus the search on the region most preferred by the decision maker, and thereby speeds up convergence.
AB - This paper proposes the Necessary-preference-enhanced Evolutionary Multiobjective Optimizer (NEMO), a combination of an evolutionary multiobjective optimization method, NSGA-II, and an interactive multiobjective optimization method, GRIP. In the course of NEMO, the decision maker is able to introduce preference information in a holistic way, by simply comparing some pairs of solutions and specifying which solution is preferred, or comparing intensities of preferences between pairs of solutions. From this information, the set of all compatible value functions is derived using GRIP, and a properly modified version of NSGA-II is then used to search for a representative set of all Pareto-optimal solutions compatible with this set of derived value functions. As we show, this allows to focus the search on the region most preferred by the decision maker, and thereby speeds up convergence.
U2 - 10.1007/978-3-642-01020-0_43
DO - 10.1007/978-3-642-01020-0_43
M3 - Chapter (peer-reviewed)
SN - 9783642010194
VL - 5467
T3 - Lecture notes in computer science
SP - 554
EP - 568
BT - Evolutionary multi-criterion optimization: proceedings of the 5th international conference
A2 - Ehrgott, M.
A2 - Fonseca, C.
A2 - Gandibleux, X.
A2 - Hao, J.
A2 - Sevaux, M.
PB - Springer
CY - Berlin
ER -