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|Title:||Multi-objective optimisation with multiple preferred regions|
|Citation:||Proceedings of the 3rd Australasian Conference on Artificial Life and Computational Intelligence (ACALCI 2017), 2017 / Wagner, M., Li, X., Hendtlass, T. (ed./s), vol.10142, pp.241-253|
|Publisher Place:||Cham, Switzerland|
|Series/Report no.:||Lecture Notes in Computer Science; 10142|
|Conference Name:||3rd Australasian Conference on Artificial Life and Computational Intelligence (ACALCI 2017) (31 Jan 2017 - 02 Feb 2017 : Geelong, AUSTRALIA)|
|Md. Shahriar Mahbub, Markus Wagner and Luigi Crema|
|Abstract:||The typical goal in multi-objective optimization is to find a set of good and well-distributed solutions. It has become popular to focus on specific regions of the objective space, e.g., due to market demands or personal preferences. In the past, a range of different approaches has been proposed to consider preferences for regions, including reference points and weights. While the former technique requires knowledge over the true set of trade-offs (and a notion of “closeness”) in order to perform well, it is not trivial to encode a non-standard preference for the latter. With this article, we contribute to the set of algorithms that consider preferences. In particular, we propose the easy-to-use concept of “preferred regions” that can be used by laypeople, we explain algorithmic modifications of NSGAII and AGE, and we validate their effectiveness on benchmark problems and on a real-world problem.|
|Description:||Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 10142)|
|Rights:||© Springer International Publishing AG 2017|
|Appears in Collections:||Computer Science publications|
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