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Learning value functions in interactive evolutionary multiobjective optimization
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Branke, Jürgen, Greco, Salvatore, Slowinski, Roman and Zielniewicz, Piotr (2015) Learning value functions in interactive evolutionary multiobjective optimization. IEEE Transactions on Evolutionary Computation, 19 (1). pp. 88-102. doi:10.1109/TEVC.2014.2303783
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Official URL: http://dx.doi.org/10.1109/TEVC.2014.2303783
Abstract
This paper proposes an interactive multiobjective evolutionary algorithm (MOEA) that attempts to learn a value function capturing the users’ true preferences. At regular intervals, the user is asked to rank a single pair of solutions. This information is used to update the algorithm’s internal valuefunction model, and the model is used in subsequent generationsto rank solutions incomparable according to dominance. This
speeds up evolution toward the region of the Pareto front that is most desirable to the user. We take into account the most general additive value function as a preference model and we empirically compare different ways to identify the value function that seems to be the most representative with respect to the given preference information, different types of user preferences, and different ways to use the learned value function in the MOEA. Results on a number of different scenarios suggest that the proposed algorithm works well over a range of benchmark problems and types of user preferences.
| Item Type: | Journal Article | ||||
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| Divisions: | Faculty of Social Sciences > Warwick Business School > Operational Research & Management Sciences Faculty of Social Sciences > Warwick Business School |
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| Journal or Publication Title: | IEEE Transactions on Evolutionary Computation | ||||
| ISSN: | 1089-778X | ||||
| Official Date: | February 2015 | ||||
| Dates: |
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| Volume: | 19 | ||||
| Number: | 1 | ||||
| Page Range: | pp. 88-102 | ||||
| DOI: | 10.1109/TEVC.2014.2303783 | ||||
| Status: | Peer Reviewed | ||||
| Publication Status: | Published | ||||
| Access rights to Published version: | Restricted or Subscription Access |
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