The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Reference point based multi-objective optimization using evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Dynamic Crowding Distance?A New Diversity Maintenance Strategy for MOEAs
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 01
An interactive territory defining evolutionary algorithm: iTDEA
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
Modeling decision-maker preferences through utility function level sets
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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In this paper an interactive method for modeling the preferences of a Decision-Maker (DM) is employed to guide a modified version of the NSGA-II algorithm: the Interactive Non-dominated Sorting algorithm with Preference Model (INSPM). The INSPM's task is to find a non-uniform sampling of the Pareto-optimal front with a detailed sampling of the DM's preferred regions and a coarse sampling of the non-preferred regions. In the proposed technique, a Radial Basis Function (RBF) network is employed to construct a function which represents the DM's utility function using ordinal information only, extracted from queries to the DM. The INSPM algorithm calls the DM's preference model via a Dynamic Crowding Distance (DCD) density control method which provides the mechanism for increasing the sampling in the preferred regions and for decreasing it in non-preferred regions which allows a fine-tunning control of the Pareto-optimal front sampling density.