Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/111349
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Type: Conference paper
Title: Multi-objective optimisation with multiple preferred regions
Author: Mahbub, M.
Wagner, M.
Crema, L.
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: Springer
Publisher Place: Cham, Switzerland
Issue Date: 2017
Series/Report no.: Lecture Notes in Computer Science; 10142
ISBN: 3319516906
9783319516905
ISSN: 0302-9743
1611-3349
Conference Name: 3rd Australasian Conference on Artificial Life and Computational Intelligence (ACALCI 2017) (31 Jan 2017 - 02 Feb 2017 : Geelong, AUSTRALIA)
Statement of
Responsibility: 
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
RMID: 0030064366
DOI: 10.1007/978-3-319-51691-2_21
Grant ID: http://purl.org/au-research/grants/arc/DE160100850
Published version: http://www.springer.com/gp/book/9783319516905
Appears in Collections:Computer Science publications

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